Browsing by Author "Bhattacharya, Kankar"

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A Comprehensive Process for Addressing Market Power in Decentralized ADN Electricity Markets
(University of Waterloo, 2025-03-05) AboAhmed, Yara; Salama, Magdy; Bhattacharya, Kankar
Electric power systems have transformed globally, with distribution grids evolving into active distribution networks (ADNs), altering their characteristics and operations. Traditional centralized market structures have become inadequate for the complexities of the ADNs, leading to inefficiencies and challenges in reliable operation and energy pricing. ADN electricity markets offer a solution by leveraging smart grid features to integrate distributed energy resources (DERs), allowing non-utility entities, such as producers, consumers and prosumers, to participate directly, enhancing market efficiency, reducing monopoly power, and limiting utility control over prices. However, with the increasing penetration of DERs, there is a growing risk of market concentration and manipulation by entities owning large shares of DERs in ADN electricity markets. This poses a potential threat to market fairness, as some participants may exploit market power, leading to an uneven playing field, reducing the integrity and efficiency of ADN electricity markets. From this standpoint, this thesis investigates and adapts the concept of market power within ADN electricity markets, considering the unique characteristics of the market and the system. The investigation is structured around six central questions: (1) Can non-utility entities exercise market power in ADN electricity markets? (2) Is there a comprehensive framework for accurately monitoring, evaluating, and mitigating market power in decentralized ADN markets? (3) If such a framework exists, can it manage the complexity of monitoring the large number of ADN market participants? (4) If market power manipulation exists, are current investigations adequate, considering the decentralized market structure, the physical characteristics of the system, DER operational constraints, and the interplay between active and reactive power markets? (5) What types of decentralized market structures and frameworks—such as fully decentralized, community-based, or network-based peer-to-peer (P2P)—are appropriate for addressing market power in ADN electricity markets? (6) Are traditional market power mitigation methods applicable and effective in the context of ADN electricity markets considering the decentralized nature of the ADN and the dispersed DERs?. The primary objective of this thesis is to develop a fair and decentralized energy trading platform that limits monopoly power and mitigates market power abuse in ADN electricity markets. To achieve this goal, the thesis proposes an innovative comprehensive process for monitoring, evaluating, and mitigating market power, specially designed for the decentralized structure of ADNs and their market frameworks. This process considers the shifts in network configuration as well as the physical and operational characteristics of ADNs and their components. The process begins by monitoring market power of dominant market participants through introducing the zoning concept. These operational zones narrow down the number of market participants within each zone, addressing the challenge of monitoring a large number of market participants with widely distributed DERs and improving the identification and control of potential market power exercisers, thus minimizing their potential market power. These operational zones serve as decentralized interfaces between the zonal market participants and their corresponding zonal market operators, establishing a decentralized platform for energy trading. The second stage of the process focuses on evaluating market power through investigating and analyzing the strategic offering behavior of the potential market power exercisers identified in stage one. This analysis is conducted within the framework of a community-based P2P decentralized ADN electricity market, considering the physical and operational characteristics of both the system and DERs, along with the coupled active and reactive power markets. A comparative evaluation of market outcomes under competitive and strategic conditions is performed to identify strategic manipulators. In this context, the study also examines the applicability and effectiveness of conventional market power mitigation techniques used for the centralized market and assesses their impact on the strategic offering behavior of identified manipulators. While some traditional market power mitigation techniques may demonstrate efficiency, a new approach is necessary to address the unique decentralization characteristic of ADN electricity markets. A novel market power mitigation technique is proposed in the third stage of the process, targeting the root cause of market power: market concentration. This approach introduces an innovative market zoning concept, dynamically partitioning the system into "Market-Zones" to reduce market concentration while adapting to different system operational conditions, considering the uncertainties in system demand and generation, thereby aligning with the decentralized nature of ADNs and their markets. The proposed innovative zoning approach offers a robust solution for mitigating market power in decentralized ADN electricity markets. Within these Market-Zones, each player can actively engage and participate in the market and obtain the benefit without being overtaken by entities with large market shares. Consequently, the market power of the dominant players is subsided and diluted by utilizing the proposed Market-Zones, establishing a fair energy trading platform.
A Review of Modeling and Applications of Energy Storage Systems in Power Grids
(Institute of Electrical and Electronics Engineers (IEEE), 2022-03-25) Calero, Fabian; Cañizares, Claudio A.; Bhattacharya, Kankar; Anierobi, Chioma; Calero, Ivan; Zambroni de Souza, Matheus F.; Farrokhabadi, Mostafa; Guzman, Noela Sofia; Mendieta, William; Peralta, Dario; Solanki, Bharatkumar V.; Padmanabhan, Nitin; Violante, Walter
As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.
Affine Policies and Principal Components Analysis for Self-Scheduling in CAES Facilities
(Institute of Electrical and Electronics Engineers (IEEE), 2022-07-26) Zambroni de Souza, Matheus F.; Cañizares, Claudio A.; Bhattacharya, Kankar; Lorca, Alvaro
This paper presents a novel methodology based on Principal Components Analysis (PCA) and Affine Policies (AP) for self-scheduling of a price-taker Compressed Air Energy Storage (CAES) facility operating under uncertainties. The proposed PCA-AP model is developed from the facility owner's perspective, which partakes in energy, spinning, and idle reserve markets. A methodology is proposed to select the required price uncertainty intervals from actual data based on a Box Cox technique. For a more realistic representation, the detailed thermodynamic characteristics of the CAES facility are considered, taking into account as well modern CAES facilities that may charge and discharge concurrently. To validate the proposed PCA-AP model and approach, the results obtained are compared with an existing Affine Arithmetic (AA) model, which is also based on an affine approach, and Monte Carlo Simulations (MCS), which can be considered as the benchmark for comparison purposes. The input data, forecast prices and intervals of uncertainty, are taken from the Ontario-Canada electricity market for 2015-2019. From the studies presented, it can be observed that the new PCA-AP approach provides less conservative results as compared to the AA approach, and hence can be considered an adequate methodology for day-ahead operations in systems with significant sources of uncertainty.
Behind-the-Meter Compressed Air Energy Storage Feasibility and Applications
(University of Waterloo, 2019-05-30) Anierobi, Chioma Christiana; Canizares, Claudio; Bhattacharya, Kankar
In many jurisdictions, commercial and industrial (C&I) customers are charged for their energy consumption as well as the power drawn from the grid at peak load hours. In Ontario, the demand-based charge component of the electricity cost has been skyrocketing, and this cost often accounts for a significant portion of the overall operating cost of large customers. The Ontario Government in 2010 launched the Industrial Conservation Initiative (ICI) program which requires large customers (Class A) to pay a Global Adjustment (GA) charge, based on their percentage contribution in load during the top five system peak load hours over a one-year base period. This offers enormous savings opportunity to many industrial customers by using strategies to reduce or offset their load during these system peak load hours. However, managing demand can be challenging when faced with production constraints in areas of high-energy sensitive production lines where short interruptions are not permitted. Energy Storage System (ESS) offers the customer the capability to carry out its usual operations while simultaneously saving on the electricity bill through demand reduction. ESS can provide electricity to the facility during system peak periods to reduce the power drawn from the grid, while during non-peak price periods, the ESS is recharged by harnessing the low-cost power. In this work, a detailed operations model of behind-the-meter Small Scale Compressed Air Energy Storage (SS-CAES) is developed for an industrial customer, with an existing well/cavern that can be re-purposed for air storage. The developed optimization model manages the operation of the CAES facility to minimize electricity costs, determining the storage energy output and the corresponding charging and discharging decisions of the SS-CAES system. Furthermore, a detailed economic analysis is carried out to examine financial viability of a practical behind-the-meter SS-CAES project. Some key parameters such as life cycle, CAES capacity and capital cost, and electricity price are considered for carrying out a sensitivity analysis, and the results suggest that SS-CAES is economically viable in the current Ontario rate structure. It is shown that the cost of an SS-CAES project and GA charges are the key determining factors for economic deployment of SS-CAES in Ontario.
A Decentralised Transactive Energy Market Considering Physical System Constraints
(University of Waterloo, 2022-08-29) Pankhurst, Colton; Bhattacharya, Kankar; Canizares, Claudio
Increasing levels of Distributed Energy Resources (DERs) are expected to play a key role in achieving global electricity decarbonisation goals, providing both a challenge and an opportunity for the electricity industry. Conventional approaches such as Net Energy Metering (NEM) have been questioned regarding their effectiveness in properly rewarding DERs, and larger efforts around the integration of DERs into wholesale markets do not address potential value streams at the distribution system level. Local energy markets leveraging direct Peer-to-Peer (P2P) trading have been proposed as a solution, which can increase prosumer participation in lower cost and more reliable supply of energy to consumers. Many approaches have been proposed to determine the optimal dispatch of distributed resources; however, a gap remains in the research to date on how to efficiently allow for prosumer decision autonomy while ensuring that the physical layer of the power system is considered. This thesis proposes a decentralised transactive solution that retains prosumer negotiation and decision autonomy, while using network operator and market determined prices to allocate limited system resources for a feasible, locally optimal system state. Peer-to-Utility (P2U) transactions are added to existing P2P energy frameworks to obtain transactive local peer decision criteria considering Peer-Centric (PC) and System-Centric (SC) objectives. Peers are able to interact with wholesale electricity market derived prices through P2U transactions, allowing for consideration of net export value in welfare maximising decisions. The proposed approach includes a split transaction fee pricing mechanism for virtual prosumer interactions that considers the networks characteristics such as topology and operational constraints to ensure consideration of the physical layer in peer decision making. In addition to pricing mechanisms for coupling the virtual and physical layers, a congestion clearing process is proposed, which coordinates with the decentralised transaction matching process and the Network Usage Charges (NUCs) to ensure efficient allocation of network capacity. Previously reported distribution networks are used to compare the transaction decisions, economic performance, and system performance of the proposed solution with existing approaches. The results demonstrate the effectiveness of the proposed method in ensuring system feasible, locally optimal transaction sets with prioritisation of local peers.
Demand Response and Battery Energy Storage Systems in Electricity Markets: Frameworks & Models
(University of Waterloo, 2019-08-09) Padmanabhan, Nitin; Bhattacharya, Kankar; Ahmed, Mohamed
Ensuring a balance between the generation and demand is one of the most challenging tasks in power systems because of contingencies, sudden load changes, forecasting errors and other disturbances, occurring from time to time. The peak demand, which occurs only for a short duration, has always been a concern for independent system operators (ISOs), as it leads to high market prices and reliability concerns. Furthermore, in recent years there have been significant increase in the penetration of renewable energy sources (RES) to address the challenge of significantly reducing carbon dioxide (CO2) and other greenhouse gas (GHG) emissions and the system's dependence on fossil fuels based generation resources. However, the high penetration of RES, because of their intermittency and uncertainty, poses operational and reliability issues and thus necessitates an increase in the procurement and deployment of primary and secondary regulation reserves, as well as spinning and non-spinning reserves. In recent years, demand response (DR) and battery energy storage systems (BESS), because of their characteristic features such as fast response time, high ramp rate, and the ability to provide flexible upward and downward response as compared to conventional generators, have been considered as promising and viable options by the ISO to reduce the peak demand, facilitate RES integration and for the provision of ancillary services, such as regulation and spinning reserves. Despite the benefits and the growth opportunities of DR and BESS, there are still many challenges associated with their market participation. To address the challenges pertaining to DR and BESS participation in electricity markets, this thesis proposes appropriate models and frameworks, which can efficiently integrate these resources into the day-ahead and real-time electricity markets, and at the same time effectively address the aforementioned challenges of ISOs. This thesis first presents a new bid/offer structure for DR provisions, simultaneously through price responsive demand (PRD) based bids and load curtailment based DR offers from customers. Thereafter, incorporating the DR offer structure, a novel day-ahead, co-optimizing market auction framework and mathematical model for DR-energy-spinning reserve market, based on LMPs, which includes transmission loss representation within the dc power flow constraints is proposed. The impact of DR on both energy and spinning reserve market prices, market dispatch, line congestions, and other economic indicators, is studied using the IEEE Reliability Test System (RTS), by considering various scenarios and cases. In the next stage, the thesis considers the BESS participation in the day-ahead markets. First, a novel BESS cost function model, considering Degradation Cost, based on depth of discharge (DOD) and discharge rate, and Flexibility Cost, in terms of the battery power-to-energy (P/E) ratio, is presented. A detailed bid/offer structure based on the proposed cost functions is formulated. Thereafter, a new framework and mathematical model for BESS participation in an LMP-based, co-optimized, day-ahead energy and spinning reserve market, have been developed. Three case studies are presented to investigate the impact of BESS participation on system operation and market settlement. The proposed model is validated on the IEEE RTS to demonstrate its functionalities. Finally, the thesis considers BESS participation in the real-time operations. Firstly, a novel framework for simultaneously procuring primary and secondary regulation reserves alongside energy, in a BESS integrated electricity market, by taking into account probabilistic scenarios of contingencies, is proposed. Thereafter, an appropriate mathematical model is developed considering BESS alongside conventional generators to determine the optimal real-time primary and secondary regulation reserves and energy market clearing, in a co-optimized, LMP based market, taking into consideration the a priori cleared day- ahead market schedules. Lastly, the impact of participation of BESS in day-ahead and real-time energy and reserve markets on prices, market clearing dispatch, and other economic indicators are investigated using the IEEE RTS, for various scenarios and cases.
Distribution System Planning in Smart Grids to Accommodate Distributed Energy Resources and Electric Vehicles
(University of Waterloo, 2017-07-05) Bin Humayd, Abdullah; Bhattacharya, Kankar
Major changes in planning paradigms have taken place in power systems in recent years because of deregulation of the power industry, environmental policy changes, advancements in technology, and the transformation of the grid to intelligent systems, referred to as the smart grid. These changes will continue to drive the distribution systems planning function to evolve in the coming years. It is therefore important to develop effective planning strategies to identify the qualities, capabilities, and attributes that are necessary for the future distribution grid. Demand response (DR), distributed generation (DG), energy storage systems (ESS), and plug-in electric vehicles (PEV) are expected to be a part of the solution of these distribution system planning challenges. However, very little of the present research on distribution system planning have considered these options simultaneously. Moreover, traditional planning options such as substation expansion, new feeder connections and capacitor placements should also be simultaneously considered. Such a coordinated planning can help evaluate the alternatives to provide maximum benefits to the network owner and customers. With the increase in gas prices driven by a foreseeable fossil fuel depletion in the future, development in the automotive sector, and environmental concerns, penetration of PEVs has been increasing in recent times. The charging load of PEVs will definitely impact the distribution grid. To mitigate these effects, the local distribution companies (LDCs) need to adopt the right actions and policies, and develop associated infrastructure. In the current context of smart grids, the LDCs need to control PEV charging demand while also considering customer preferences, which can lead to benefits such as deferment of the decisions on reinforcement and other investments, and maximize the use of existing infrastructure. In addition, LDCs need to establish rate structures that incentivize the use of smart charging and increase the adoption and use of PEVs, which can benefit both the LDCs and the customer. This research focuses on developing models to investigate and address the problem of distribution system planning in the presence of PEV charging loads. First, a comprehensive long-term distribution planning framework from the perspective of LDCs is proposed considering DG, substations, capacitors, and feeders. Apart from considering the usual demand profile, the proposed framework considers uncontrolled and controlled (smart) PEV charging demand, as well as DR options. Based on a back-propagation algorithm combined with cost-benefit analysis, a novel approach is proposed to determine the optimal upgrade plan, allocation, and sizing of the selected components in distribution systems, to minimize the total capital and operating cost. A new iterative method is proposed which involves post-processing the plan decisions to guarantee acceptable adequacy levels for each year of the planning horizon. Second, a generic and novel framework is proposed to assess the Distribution System Loading Margin (DSLM) to accommodate uncontrolled and smart PEV charging loads without the need for any additional investments or upgrades in the distribution system. The model determines what percentage of the fleet can be served by uncontrolled charging and smart charging, respectively. Monte Carlo simulation has been carried out to simulate the uncertainty of demand, drivers' behaviour, market share of PEV class, and charging level. The maximum allowable penetration of uncontrolled and smart charging loads are determined based on the current available market data pertaining to PEV type and charging level, considering different charging scenarios. Finally, a PEV smart charging approach is proposed where the charging loads are incentivized by the LDC for every unit of energy controlled. A novel framework is proposed to determine the optimal participation of PEVs in the smart charging program and optimal incentives paid by the LDC to PEV customers, such that both parties are economically benefited. The proposed framework models the relationship between customers' participation and incentives offered by the LDC. The relationship between the expected investment deferral and hence the economic benefits from smart charging participation are considered as well. Monte Carlo simulation is carried out to simulate the uncertainty of demand, electricity market price, drivers' behaviour, PEV market share, and charging level.
Dynamic Modelling and Performance Analysis of Energy Storage Systems for Frequency Regulation in Bulk Power Systems
(University of Waterloo, 2021-09-21) Guzman Encalada, Noela Sofia; Canizares, Claudio; Bhattacharya, Kankar
Renewable Energy Sources (RESs) provide a feasible alternative to supply electrical loads without the unfavorable environmental impacts of fossil fuels. However, despite the signiﬁcant environmental beneﬁts of RESs, several operational challenges associated with their high levels of penetration in power systems need to be addressed. Extensive research has shown that Energy Storage Systems (ESSs) facilitate increased penetration levels of RESs by providing ﬂexibility to the system, especially considering the technical maturity and decreasing cost of these technologies; hence, penetration of ESS, such as batteries and ﬂywheels is likely to grow signiﬁcantly in the coming years. Indeed, services that have been traditionally procured from synchronous generators such as Frequency Regulation (FR) are already being provided by ESSs. However, appropriate frequency control must be considered to take advantage of the fast response capability of ESS facilities, while coordinating their response with the bulk conventional generators currently used for FR. Some characteristics of the bulk power grids, regulation signals, and the State of Charge (SoC) management of the ESSs need be considered for the design of proper FR controls. In this thesis, a FR model is proposed of a large interconnected power system including ESSs such as Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs), considering all relevant stages in the frequency control process. The model, which considers Communication Delays (CDs) in the transmission of signals in the FR control loop, is developed from the viewpoint of an Independent System Operator (ISO), using the Ontario Power System (OPS) as case study. To this eﬀect, empirically-based and generic SoC models for FESS and BESS considering the charging and discharging process characteristics are proposed. The system, ESSs, and SoC components are modelled in detail from a FR perspective and validated using real system and ESSs data, and a practical transient stability model of the North American Eastern Interconnection (NAEI) in Dynamic Security Assessment Tools (DSATools™) platform. The proposed model is validated with and considers all main stages of the FR control process, including CDs and the SoC management model of the ESS facilities, ensuring a realistic closed-loop response. Simulation studies show that the proposed model accurately represents the FR process of a large interconnected power system including ESSs, and can be used for accurate FR studies. The impact of CDs and SoC management of ESS facilities on the Area Control Error (ACE), and the computational eﬃciency of the proposed FR model are studied and discussed. A novel H2 ﬁlter design is proposed to optimally split the FR signal between conventional and fast regulating ESS assets, considering typical CDs. The design approach includes ﬁltering the FR signal by producing a slowly-varying component or Traditional Regulation Signal (RegA) to be provided to the slow regulating resources (i.e., Traditional Generators (TGs)), while the remaining fast component or Dynamic Regulation Signal (RegD) is provided to the fast response ESS facilities (FESS and BESS) to take advantage of their fast response characteristics. The design of the H2 ﬁlter is formulated as an optimal control design problem, and the proposed ﬁlter is integrated into the previously validated FR model with ESSs to form an Integrated Model, which includes a Proposed Set-Point (PSP) calculation and an anti-windup strategy. The PSP allows FR capacity from ESSs to be comparable to TGs FR capacity while keeping the system stable, which is not the case in the current FR process for the OPS. The proposed anti-windup strategy is added to avoid saturation when both TGs and ESSs reach their limits, or TGs reach their limits while the ESS facilities are not able to follow the PSP signals because of their SoC limits. Thus, the proposed ﬁlter sends RegA and RegD signals considering the SoC of fast response resources and capacity limits of ESSs and TGs, and depend on the conditions of the system, working in a coordinated manner. The FR performance with the H2 ﬁlter signals, RegA and RegD, is also compared with the existing FR process in the OPS, focusing on studying the impact of CDs and limited regulation capacity, and the eﬀect of the PSP calculation and anti-windup strategy. The results show that the H2 ﬁlter design and signal splitting strategy improves the FR process performance signiﬁcantly, in terms of reducing the ACE, and thus reduce the need for regulation capacity. Finally, a detailed methodology is developed to obtain Marginal Rate of Technical Substitution (MRTS) curves for the Independent Electricity System Operator (IESO). The IESO’s MRTS curves consider diﬀerent ESSs and discharging times (i.e., 15 min for FESS, and 15 min, 1 h, 2 h, and 4h for BESS), scenarios (i.e., peak hours, non-peak hours, morning ramp hours, and evening ramp hours), and seasons. The criteria agreed upon with the IESO for the generation of heat maps and MRTS is also presented. Furthermore, the procedure to select the representative typical days per season to be used in the generation of the MRTS curves is explained in detail, and an example of how to interpret one of the MRTS curves is explained. Heat maps and MRTS curves are proposed as analysis tools to allow ISOs to select the desired performance metric, and the combination of RegA and RegD resources that would allow to achieve it while still reducing the total regulation capacity. Although this methodology is applied to the IESO, it could be applied to other ISOs with appropriate modiﬁcations.
Electricity Market Participation and Investment Planning Frameworks for Energy Storage Systems
(University of Waterloo, 2020-08-31) Alharbi, Hisham; Bhattacharya, Kankar
The recent trend of increasing share of renewable energy sources (RES) in the generation mix has necessitated new operational and planning studies because of the high degree of uncertainty and variability of these sources. RES such as solar photovoltaic and wind generation are not dispatchable, and when there is excess energy supply during off-peak hours, RES curtailment is required to maintain the demand-supply balance. Furthermore, RES are intermittent resources which have introduced new challenges to the provision of ancillary services that are critical to maintaining the operational reliability of power systems. Energy storage systems (ESS) play a pivotal role in facilitating the integration of RES to mitigate the aforementioned issues. Therefore, there is a growing interest in recent years to examine the potential of ESS in the future electricity grids. This research focuses on developing market participation and investment planning frameworks for ESS considering different ownership structures. First, a novel stochastic planning framework is proposed to determine the optimal battery energy storage system (BESS) capacity and year of installation in an isolated microgrid using a novel representation of the BESS energy diagram. A decomposition-based approach is proposed to solve the problem of stochastic planning of BESS under uncertainty. The optimal decisions minimize the net present value of total expected costs over a multi-year horizon considering optimal BESS operation using a novel matrix representing BESS energy capacity degradation. The proposed approach is solved in two stages as mixed integer linear programming (MILP) problems; the optimal ratings of the BESS are determined in the first stage, while the optimal installation year is determined in the second stage. Extensive studies considering four types of BESS technologies for deterministic, Monte Carlo Simulations, and stochastic cases are presented to demonstrate the effectiveness of the proposed approach. The thesis further studies the investment decisions on BESS installations by a third-party investor in a microgrid. The optimal BESS power rating, energy capacity, and the year of installation are determined while maximizing the investor's profit and simultaneously minimizing the microgrid operational cost. The multi-objective problem is solved using a goal programming approach with a weight assigned to each objective. The BESS is modeled to participate in energy arbitrage and provision of operating reserves to the microgrid, considering its performance parameters and capacity degradation over the planning horizon. Finally, in the third problem addressed in the thesis in the context of electricity markets, the non-strategic and strategic participation of a pumped hydro energy storage (PHES) facility in day-ahead energy and performance-based regulation (PBR) markets, which includes regulation capacity and mileage, are examined. The PHES is modeled with the capability of operating in hydraulic short-circuit (HSC) mode with detailed representation of its operational constraints, and integrated with an energy-cum-PBR market clearing model. For its strategic participation, a bi-level market framework is proposed to determine the optimal offers and bids of the PHES that maximize its profit. The operation of PHES is modeled at the upper level, while the market clearing is modeled in the lower level problem. The bi-level problem is formulated as a mathematical programming with equilibrium constraints (MPEC) model, which is linearized and solved as an MILP problem. Several case studies are carried out to demonstrate the impact of PHES' non-strategic and strategic operations on market outcomes. Furthermore, stochastic case studies are conducted to determine the PHES strategies considering the uncertainty of the net demand and rivals' price and quantity offers.
Emerging Electricity Markets: Including New Energy Storage Technologies & Integrating DERs via ISO-DSO Coordination
(University of Waterloo, 2024-08-22) Goyal, Anshul; Bhattacharya, Kankar
Most countries have set a vision of net-zero Greenhouse Gas (GHG) emissions by 2050; however, based on current trends, many of them are lagging in meeting the targets, even for 2025. Energy transition, shifting from fossil-fuel based to clean resources, is a critical step toward achieving Net-Zero Emission (NZE) targets, and is being explored worldwide. The ongoing effort to support the transition to a decarbonized system is to deploy large-scale Renewable Energy Sources (RES); but even after the remarkable increase in deployment of RES, it still seems impossible to achieve decarbonization targets. Various countries, including Canada, have pledged to achieve NZE grid by 2035 and system operators have developed their decarbonization pathways with target objectives and timelines to attain this goal. In this context, green hydrogen-based systems emerge as a potential zero-carbon solution to meet the CO2 emission reduction targets. The electricity sector is recognized as vital for energy sector transformation, in order to achieve NZE goals, as there are already low and emission free resources in this sector such as RES, hydro, etc., and it can easily integrate with other sectors (heat, transport, etc.) as part of the electrification drive. The continuously growing demand for electricity is a challenge to energy security, grid resiliency and results in exorbitant energy prices during peak demand periods. The intermittent nature of RES imposes limits on their use and their variability leads to imbalances between the grid demand and supply. To meet these challenges, the power system requires flexible resources, for which, various alternatives have been proposed including Distributed Energy Resources (DERs), Demand Response (DR) and Energy Storage Systems (ESSs), which seem to be the most promising ones. Also, there have been remarkable advancements in the arena of smart grid, which encourages consumers to deploy DERs and re-profile themselves as prosumers. Different regulating bodies and utilities worldwide are re-organizing their electricity markets to be future-ready with high-DER vision, and are developing coordination models between the Independent System Operator (ISO) and Distribution System Operators (DSOs) to integrate DERs and realize their true potential for the whole system (transmission and distribution). This thesis first presents a novel, Green Hydrogen Systems (GHSs) integrated, Uniform Marginal Price (UMP)-based Day-Ahead Market (DAM) framework and the mathematical model for electricity market auction. The wholesale electricity market participation of GHSs, comprising electrolyzers, storage tanks and fuel cells, is examined considering their bids and offers for charging and discharging modes, respectively. To support transition toward achieving an NZE grid, the effects of inclusion of GHS in the DAM with different emission control strategies, such as emission cap and carbon pricing are examined. Two real systems with distinct characteristics, Alberta and Ontario provinces of Canada, are considered. Subsequently, this thesis presents an extension of the GHS integrated UMP-based market model to Hydrogen-based Emission Free Resources (HEFRs) included Locational Marginal Price (LMP)-based DAM model by formulating appropriate mathematical model, complying with the existing market rules. Comprehensive case studies and sensitivity analysis are carried out to examine the impact of integration of HEFRs on market settlement, marginal prices and system emissions during normal, congestion and under RES uncertainties scenarios. Next, this thesis presents a novel framework with new mathematical models that integrate DR and Battery Energy Storage Systems (BESSs) simultaneously in an LMP-based Multi-Settlement Market (MSM), i.e. a coordinated DAM and Real-Time Market (RTM). A new set of generator ramping constraints, developed from the DAM settlement, and referred to as Day-Ahead Load-Following (DALF) Ramp, are included in the RTM auction model. The performance of the mathematical models are tested on the IEEE 24-bus Reliability Test System (RTS) by carrying out various case studies and scenarios, uncertainty and sensitivity analyses. Effect of DR and BESS characteristics such as level of participation, initial State-of-Charge (SOC), discharge rate, etc. on market settlement is examined. The results demonstrate the merits of the proposed framework, and the impact of the DALF Ramp, DR and BESS inclusion in the MSM auction models on marginal prices, market settlement and system operation. Finally, the thesis presents a new Cooperation Algorithm and a parallel-hierarchical framework for coupling the wholesale and retail electricity markets in order to facilitate the participation of DERs including small-capacity Behind-the-meter DERs (BTM-DERs), in a competitive and equitable manner. The detailed mathematical models of ISO-DSOs coordinated, wholesale and retail market settlements for day-ahead period are developed. The models are tested on the IEEE 24-bus RTS (wholesale market) and multiple 33-bus distribution systems (retail markets). Results demonstrate the effectiveness of the proposed framework over a centralized wholesale market in terms of computational time and over the sequential structure, in terms of DERs’ increased participation, reduced market prices, congestion management, emissions reduction and overall system operation.
Energy Management and Demand Response of Industrial Systems
(University of Waterloo, 2018-11-26) Alarfaj, Omar; Bhattacharya, Kankar
Energy management is an important concept that has come to the forefront in recent years under the smart grid paradigm. Energy conservation and management can help defer some capacity addition requirements in the long-term, which is very significant in the context of continuously growing demand for energy. It can also alleviate the adverse environmental impacts of commissioning new generation plants. Therefore, there is a continuous need for the development of appropriate tools to ensure efficient energy usage by existing and new loads and the efficient integration of distributed energy resources (DER). There is a need for energy conservation in the industrial sector as it accounts for the largest share of energy consumption among all customer sectors. Also considering their high energy density, industrial facilities have significant potential for participating in demand side management (DSM) programs and help in reducing the system peak demand by reducing or shifting their load in response to energy price signals. However industrial demand response (DR) is typically constrained by the operational requirements such as process interdependencies and material flow management. An EMS framework is proposed in this thesis for optimal load management of industrial loads which includes improved load estimation technique and uncertainty mitigation using MPC. The framework has been applied to a water pumping system (WPS) where an equipment level load modeling is implemented using a NN-based model. Another EMS framework is proposed for an oil refinery process. The refinery EMS is developed based on power demand modeling of the oil refinery process, considering an on-site cogeneration facility. A joint electrical-thermal model is proposed for the cogeneration units to account for the electricity and steam production costs. In addition to load management, DR for industrial loads is investigated as another energy management application. However since DR requires interaction between the energy supplier and the customer, this thesis considers DR from both the local distribution company's (LDC) and industrial customer's perspectives. From the LDC's perspective, the objective is to reduce the network operational costs by minimizing peak demand and flattening the load profile for better utilization of system resources. From the industrial customer's perspective, the objective is to minimize the energy cost using both load management decisions and DR signals sent by the LDC. While the developed EMS models are used to represent the industrial customer's operations, a distribution optimal power flow (DOPF) model is developed to represent distribution system operations. The DR strategy proposed in this thesis is based on effective communication between the customer's EMS and the LDC's operations using a day-ahead contractual mechanism between the two parties, and a real-time operational scheme to mitigate the uncertainties through improved forecasts for energy prices and power demand. Two types of DR signals are proposed; a desired demand profile signal and a retail price signal, which are developed by the LDC and sent to the customer to achieve the desired DR in a collaborative manner. In the retail price based control approach, the signal is produced by a retail pricing model which is designed based on customer's historical data collected by the LDC.
Flexibility Provisions from Energy Hubs for Sustainable Energy Systems
(University of Waterloo, 2018-09-25) Alharbi, Walied; Bhattacharya, Kankar
Power systems have some inherent level of ﬂexibility built into the system, to meet the continuous mismatches between the supply and demand. Variability and uncertainty are not new to power systems as loads change over time and generators can fail in unpredictable manners. Penetration of renewable resources and plug in electric vehicles (PEVs) can make this mismatch even more diﬃcult to meet and new ﬂexibility resources will be needed to supplement the ﬂexibility capabilities of the existing system. There are many options to provide ﬂexibility at the distribution system level, but their potential have not been fully utilized. This thesis addresses some of the pertinent issues relating to ﬂexibility provisions from energy hubs. In the ﬁrst research problem, an electric vehicle charging facility (EVCF) is transformed to operate as a smart energy hub in order to build its ﬂexibility provision. The EVCF demand mostly occurs during the evening, coinciding with the peak demand, and has no ﬂexibility because of the short stay of PEVs at the charging facility. From the system planner’s and operator’s point of view, such transformation of the EVCF presents a new source of ﬂexibility to the distribution system, which could alleviate network stress and defer upgrades, and the transformation to a smart energy hub will also reduce the EVCF’s operating costs through improved energy management. A generic and novel framework is proposed to optimally design and plan an EVCF as a smart energy hub that controls the energy ﬂow between the renewables-based generation units, the battery energy storage system (BESS), the external grid, and local consumption. The proposed framework is based on a bottom-up approach to design and planning of an EVCF, incorporating a detailed representation of vehicle mobility statistics to estimate the charging load proﬁle, and then integrating all dimensions of planning, such as technical feasibility assessment, economics, and distribution system operations impact assessment. The thesis further presents a new mathematical model to design an EVCF with distributed energy resources (DERs) to provide ﬂexibility services in wind integrated power grids. Two diﬀerent ownership structures of the EVCF and the wind generation facility (WGF) are presented and analyzed for the ﬁrst time. The DER options considered for the EVCF design are solar photovoltaic (PV) units and BESS. The eﬀects of wind power uncertainty on power system operations are mitigated through the designed EVCF with DERs via the upward and downward ﬂexibility provisions. Monte Carlo simulations are used to simulate the uncertainties in PV and wind generation, and market price. In the third research problem, residential loads are transformed to residential energy hubs (REHs) to develop an inherent ﬂexibility in their portfolios, and hence oﬀer a wide range of beneﬁts to the power grid, such as peak reduction, congestion relief and capacity deferral. A generic and novel framework is proposed, to simultaneously determine the optimal penetration of REHs in distribution systems and the optimal incentives to be remunerated by the local distribution company (LDC) to residential customers for ﬂexibility provisions, considering economic beneﬁts of both parties. The proposed framework models the relationship between the participation of residential customers in transforming their houses to REHs and the incentives to be oﬀered by the LDC. A new concept of unloaded and loaded states of REHs is also introduced for quantifying the power availability of REHs, from which power ﬂexibility can be provided considering the penetration of REHs in the system.
Flexibility Provisions from Energy Storage Systems and Loads in Smart Grid
(University of Waterloo, 2021-02-22) Alrumayh, Omar; Bhattacharya, Kankar; Wong, Steven
Electric power systems are experiencing a movement toward increasing the share of renewable energy sources (RESs), while having to cope with the retirement of conventional generating units to facilitate an eco-friendly system. However, the uncertainty and variability associated with RESs and the demand, call for additional sources of flexibility. Residential, commercial, and industrial loads are a potential source of flexibility in power systems. In addition, recent deployments of energy storage systems (ESSs) can contribute significantly to power system flexibility. Therefore, the effective management of flexible sources can lead to an improved power system operation. This thesis investigates options for capturing the flexibility of residential loads and ESSs in a power distribution system. A two-stage optimization framework is developed wherein multiple home energy management systems (HEMSs) simultaneously optimize their respective energy consumption patterns, and determines their flexibility provisions, which are communicated to the local distribution company (LDC). A flexibility evaluation approach is developed to estimate the residential energy hub (REH) flexibilities at each bus in the distribution system. Intra-hour flexibility indices are calculated to represent the REHs' willingness to alter their consumptions. Different clusters of residential customers are considered, classified by their ownership of photovoltaic (PV) panels and ESSs, and their preferred objectives. The LDC aggregates the controllable demand profiles and the flexibilities of each HEMS to optimize its operational performance and hence determines peak reduction signals that are sent to the HEMSs. Studies are carried out considering a 33-bus distribution system coordinating with 1,295 houses connected at different buses, with varying customer preferences and objectives, to demonstrate the applicability of the proposed scheme. ESSs can improve the energy management in distribution systems, especially with the increasing penetration of HEMSs that schedule household appliances and render them as smart loads. A large number of uncoordinated HEMSs can result in significant changes to the aggregated load profile of the distribution system. Therefore, a new framework and mathematical model for integrating ESSs in the distribution grid is proposed to minimize the operation cost of the LDC and to alleviate the impact of uncoordinated HEMS operation on the distribution grid. A novel neural network (NN) based state-of-health (SoH) estimator for a lithium-ion (Li-ion) battery based ESS is proposed, which is incorporated within the LDC's planning problem. The results show that the proposed estimation model is an accurate estimation of the SoH of the ESS. Also, the LDC's ESS investment plan decisions are compared considering the proposed SoH of the ESS vis-_a-vis a linear degradation model, and when degradation of ESS is not considered in planning. The third research problem addressed in the thesis investigates the ESS's role in providing the LDC with flexibility services. A novel flexibility service framework is developed based on the battery energy storage system (BESS)s' in providing different levels of charge rate (C-Rate). This work proposes a cooperative game theory based approach to determine the allocation of monetary benefits among flexible BESSs. The proposed model ensures a fair distribution of monetary gains among the coalition members and proposes a novel flexibility pricing scheme.
Impact of Distributed Battery Energy Storage on Electric Power Transmission and Distribution Systems
(University of Waterloo, 2021-01-07) Calero, Fabian; Cañizares, Claudio; Bhattacharya, Kankar
The penetration of Renewable Energy Sources (RES) in electricity grids has increased worldwide over the past decade because of their decreasing costs, especially of Photovoltaic (PV) and wind generation resources with government support for their deployment to counteract global warming effects. Indeed, nowadays, not only utility-scale, but small-scale RES connected at the distribution level are being installed by residential and industrial customers to improve their energy supply and costs. In this context, Energy Storage Systems (ESSs) can be used to facilitate the integration of RES into the grid; Battery Energy Storage Systems (BESSs) being a relatively matured and suitable storage technology for such applications. In particular, distribution systems in some jurisdictions are experiencing an increasing number of new installations of Distributed Energy Resources (DERs), including PV generation accompanied by BESSs, thus, transforming the traditionally passive utility grids into Active Distribution Networks (ADNs), whose operation has the potential to influence the transmission system upstream. Some issues associated with large quantities of DERs connected to ADNs are reduction of transmission level flexibility to accommodate changes at the distribution system, larger frequency deviations due to reduction of system inertia, and various other grid stability issues associated with DER converter interfaces. BESSs can help address some of these problems by providing grid services such as voltage control, oscillation damping, frequency regulation, and active and reactive power control. As a result, appropriate assessment of the integration of distributed DERs on transmission grids, particularly BESSs, is necessary. In this thesis, the impacts of grid-scale and distributed BESSs connected at the distribution system level, on the transmission grid are studied, for which suitable models for steady-state and dynamic analyses are proposed. Thus, first, a dynamic average BESS model is proposed, which comprises a simplified representation of the battery cells to allow simulating the effects of battery degradation, a bidirectional buck-boost converter (dc-to-dc), a Voltage Source Converter (VSC), an ac filter, and associated controls. The decoupled dq-current control of the VSC enables independent control of the BESSs’ active and reactive power injections, thus allowing their operation in several modes studied and improved in this work, namely, constant active and reactive power, constant power factor, voltage regulation, frequency regulation, oscillation damping, and a combination of the last two. The BESS average model is included within a commercial-grade software for power system analysis, validated against a detailed model that considers the high-frequency switching in the converters, and tested for different contingencies when connected to a benchmark system to demonstrate the effectiveness of a grid-scale BESS to provide the services stated earlier. In the second part of the thesis, in order to investigate the effects of distributed BESSs connected to ADNs, on the transmission grid, for dynamic electrical studies, an aggregated black-box BESS model at the boundary bus of the transmission and ADN is proposed. ADN measurements of the aggregated response of the BESSs at the boundary bus with the transmission system are used to develop the aggregated black-box model, which is based on two Neural Networks (NNs), one for active power and the other for reactive power, with their optimal topology obtained using a Genetic Algorithm (GA). Detailed simulations are performed considering multiple BESSs connected to a CIGRE benchmark and located at a load bus of the 9-bus WSCC benchmark transmission network to generate training data for the NNs. Then, the test ADN is replaced by the proposed black-box model, with aggregated models of the loads and PV generation, demonstrating that the model can accurately reproduce the results obtained for trained and untrained events. The main conclusions of this work are that the inclusion of the proposed controllers for the BESS can significantly improve the contribution of both grid-scale and distribute BESSs to the stability of transmission grids. In addition, the need of including the dc-to-dc converter in the BESS model for dynamic studies is demonstrated, especially when degraded batteries are considered, due to the limitations this operating condition creates on the dc-to-dc operation and its associated controller. Finally, the proposed methodology used to develop the black-box model to represent the aggregated response of BEESs is proved to be robust, since this model is shown to accurately reproduce the behavior the aggregated response of the battery systems providing various grid services, not only for the events and associated data used to train the proposed NN-based models, but also for contingencies for which the models were not trained.
Impact of PEV Charging Loads on Distribution System Operations and Optimal Siting and Sizing of PEV Charging Stations
(University of Waterloo, 2016-01-11) Shetty, Shubhalakshmi; Bhattacharya, Kankar
Smart grid has emerged as a promising paradigm to promote and deliver a clean, modern and efficient electricity grid to all customers, and it allows Local Distribution Companies (LDC) to integrate renewable sources more reliably, efficiently, safely and economically. Smart grid realizes Plug-in Electric Vehicles (PEVs) as a potential solution to reduce green house gas (GHG) emissions. However, large scale penetration of PEVs can significantly impact distribution system operations. This thesis first presents an extensive study of PEV characteristics such as, owner driving behavior, mobility trends of the system as a whole, battery capacity, State of Charge (SOC), different charging levels and energy required for charging the battery. The US National Household Travel Survey (NHTS) 2009 data set is explored to model the PEV load characteristics by representing customers' charging behavior in close to reality. This includes the study of the number of trips covered each day, during weekdays and weekends, over different seasons, the miles traveled, and the home arrival and departure times. Using the developed PEV load profiles, distribution system impact analysis and optimal operational studies are carried out to examine how the LDC can accommodate such loads. The NHTS data set is also used to develop probability density functions (pdfs) of certain mobility patterns such as initial SOC and starting time of charging. Using these pdfs, a Stochastic Distribution Optimal Power Flow (SDOPF) model with various objectives such as minimization of feeder loss, minimization of energy drawn and minimization of PEV charging cost, subject to feeder operational constraints is presented. Various scenarios of uncontrolled and smart charging are studied. In the uncontrolled charging case, the worst case scenarios are discussed. The smart charging scenarios provides with the optimal charging schedules which result in flattening the load profile. This thesis further presents an approach to optimally siting and sizing of Electric Vehicle Charging Stations (EVCS). Various aspects in identifying the optimal location of EVCS, from both the LDC's and customers' perspectives are discussed. A new approach to modeling the initial SOC of PEVs considering the travel distance from home to EVCS in relation to the feeder sections' electrical parameters is presented. A heuristic approach to determine the optimal siting and sizing of EVCS considering minimum feeder loss, peak demand and customer charging cost is proposed.
Improved and Practical Energy Management Systems for Isolated Microgrids
(University of Waterloo, 2018-04-10) Solanki, Bharatkumar; Bhattacharya, Kankar; Canizares, Claudio
There are many remote communities around the world which do not have interconnection with the power grid because of technical and/or economic constraints, and thus have to manage their energy requirements independently, mainly from fossil-fuel-based and in some cases renewable-based generation, operating as isolated microgrids. The reliable and economic operation of a microgrid is handled by an Energy Management System (EMS), which includes scheduling and dispatching Distributed Energy Resources (DERs) such as Distributed Generators (DG), Energy Storage Systems (ESS), with controllable loads and demand response (DR), while maintaining appropriate reserve levels, and considering uncertainty in the forecast of renewables. Thus, this thesis focuses on developing comprehensive EMSs that consider Unit Commitment (UC), and Optimal Power Flow (OPF) constraints, smart load models for DR, and possible deviations in the forecast of renewable-based DGs. First, a mathematical model of smart loads in DR schemes is developed, based on a centralized and integrated UC and OPF EMS for isolated microgrids, to optimally dispatch generation and smart loads. These smart loads are modeled by a neural network (NN) load estimator as a function of the ambient temperature, time of day, Time of Use (TOU) price, and a peak demand constraint that the microgrid operator may set. A novel Microgrid EMS (MEMS) approach based on a Model Predictive Control (MPC) technique to manage forecast uncertainties is formulated; this tool yields optimal dispatch decisions of DGs, and ESS, and obtains optimal peak demand constraints for smart loads, considering power flow and UC constraints simultaneously. The impact of DR on the microgrid operation with the developed MEMS is studied using a CIGRE benchmark system that includes DERs and renewable-based generation, demonstrating its feasibility and advantages over existing EMS approaches, and showing the benefits of controllable loads in microgrids. In isolated microgrids, the network losses and voltage drops across feeders are relatively small. This feature is utilized through a novel linearization approach applied to the unbalanced power flow equations to propose practical EMSs. The proposed EMS models are Mixed Integer Quadratic Programming (MIQP) problems, requiring less computation time and thus suitable for online applications. The proposed practical EMS models are compared with a typical decoupled UC-OPF based EMS with and without consideration of system unbalancing. These EMS models, along with ``standard" EMS models, are tested and validated, using an MPC approach to account for forecast deviations, on the CIGRE medium voltage benchmark system and the real isolated microgrid of Kasabonika Lake First Nation (KLFN) in Northern Ontario, Canada. The presented results demonstrate the effectiveness, and practicability of the proposed models. In the third stage of the thesis, the impact of Electric Thermal Storage (ETS) systems on the operation of Northern Communities' microgrids is analyzed. A mathematical model of the ETS system is developed, in collaboration with a colleague from Karlsruhe Institute of Technology, and integrated into an EMS for isolated microgrids, in which the problem is divided into UC and OPF subproblems, to dispatch fossil-fuel-based generators, ESS, and ETS charging. To account for the deviations in the forecast of renewables and demand, an MPC technique is used. The proposed ETS-EMS framework is tested and studied on a modified CIGRE medium voltage benchmark system, which comprises various kinds of DERs, and on the real KLFN isolated microgrid system. It is shown that the ETS significantly reduces operating costs, and allows for better integration of intermittent wind and solar sources. Finally, equivalent CO2 emission models for fossil-fuel-based DG units are developed considering their individual emission characteristic and fuel consumption. These models are then integrated within a microgrid EMS model, together with constant energy, and demand shifting load models, to examine the possible impact of DR on the total system emissions and economics of a microgrid, using again an MPC approach to manage forecast uncertainties. The impact of including the developed emission models on the operation of an isolated microgrid, equivalent CO2 emissions, and costs are examined considering five different operating strategies. The proposed operating strategies are validated on a modified CIGRE medium voltage benchmark system, with the obtained results highlighting the effectiveness of the proposed EMS and also demonstrate the impact of DR on emissions and costs.
Modeling and Operation of Ground Source Heat Pumps in Electricity Markets Considering Uncertainty
(University of Waterloo, 2022-11-17) Peralta Moarry, Dario; Bhattacharya, Kankar; Canizares, Claudio
Ground Source Heat Pump (GSHP) systems have grown in popularity and acceptance worldwide as an attractive option to replace conventional Heating Ventilation and Air Conditioning (HVAC) technologies due to their capacity to provide space heating and cooling in buildings and houses. Such GSHP systems may participate as a price-taker in electricity markets through a load aggregator to optimize their load demand, being able to provide grid services, such as load shifting. Therefore, aggregated GSHP systems have the potential, if properly designed, integrated, and applied, to yield energy and carbon savings in the energy market. However, the integration of such aggregated GSHP systems brings new challenges to operators, as it involves uncertainties on ambient temperature and electricity price forecasts, which can be highly volatile and thus impact the GSHP system operation and its participation in electricity markets. From a detailed literature review of GSHP applications for load management for residential users, it can be concluded that there are no works that discuss the operational performance of large-scale GSHP systems, modeled in detail, and their integration in electricity markets; additionally, none of the existing works have considered uncertainties in terms of ambient temperature and electricity price forecasts for the optimal operation of aggregated GSHP systems. After a comprehensive review of the relevant background related to GSHP systems, aggregator strategies in the electricity market, and optimization in the presence of uncertainties, in this thesis, a detailed mathematical model is presented of a GSHP with a vertical U-pipe Ground Heat eXchanger (GHX) configuration to provide residential space heating/cooling, integrating them into a load aggregator model. Based on this model, a two-stage operational strategy for the GSHP price-taker aggregator participating in Day-Ahead Market (DAM) and Real-Time Market (RTM) is proposed, to determine the optimal annual heating/cooling load dispatch to control the temperatures for a community of houses that minimizes the aggregator’s cost. Simulations are presented then of an aggregator’s optimal load dispatch with a conventional HVAC and the proposed GSHP alternative, considering comfort maximization vis-a-vis minimization of electricity costs, and showing the impact of each objective with respect to the dispatch of controllable loads, in-house temperature, and total procurement costs. Finally, a novel model based on Robust Optimization (RO) is proposed and developed, considering uncertainties in terms of the DAM and RTM electricity prices and hourly ambient temperature forecasts, which yields an optimum schedule that protects against the worst-case scenario for a given level of conservatism. The RO model is compared and validated in a realistic test system with respect to Model Predictive Control (MPC) and Monte Carlo Simulations (MCS) approaches that are traditionally used to manage uncertainty. It is shown that the proposed RO approach is computationally efficient compared to the MPC and MCS approaches, and properly accounts for the considered uncertainties, demonstrating the advantage of the presented RO technique for GSHP dispatch by aggregators.
Modeling of Compressed Air Energy Storage for Power System Performance Studies
(University of Waterloo, 2020-04-16) Calero, Ivan; Canizares, Claudio; Bhattacharya, Kankar
In the effective integration of large renewable generation for grid scale applications, pumped-storage hydro and Compressed Air Energy Storage (CAES) are currently economically and technically feasible alternatives to properly manage the intrinsic intermittency of energy sources such as wind or solar, with CAES being less restrictive in terms of its location. Furthermore, the relative fast response, and the possibility of physically decoupling the charging and discharging drive trains interfacing the grid through synchronous machines make CAES a suitable asset to provide ancillary services in addition to arbitrate, such as black start, spinning reserve, frequency regulation, and/or voltage regulation. Nevertheless, although the economic value of CAES having multiple stream revenues has been studied in the context of planning and operation of power systems, the actual impact of CAES facilities on the electrical grids have not been properly addressed in the literature, in part due to the lack of suitable models. The existing CAES models proposed for power system studies fail to represent the dynamics, nonlinear relations, and physical restrictions of the main mechanical subsystems, by proposing simplifications that result in unrealistic dynamic responses and operating points when considering the entire CAES operating range, as is required in most ancillary services or during grid disturbances. Furthermore, the detail of these models and the controls used are inconsistent with the state-of-the-art modeling of other storage technologies such as batteries and flywheels. Hence, in order to bridge the gap in CAES modeling and control, this thesis propose a comprehensive physically-based dynamic mathematical model of a diabatic CAES system, considering two independent synchronous machines as interface with the grid, which allows simultaneous charging and discharging of the cavern, such as the recently inaugurated 1.75 MW CAES plant in Goderich, Ontario. Detailed and simplified models are proposed based on the configuration of the Huntorf plant, in Germany, which is one of the only two existing large CAES facilities currently operating in the world. The system modeled comprises a multi-stage compressor with intercoolers and aftercooler, driven by a synchronous motor in the charging stage, an underground cavern as storage reservoir, a multi-stage expander with a recuperator and reheater between stages, and a synchronous generator in discharging mode, such as the aforementioned small CAES Ontario plant. The proposed thermodynamic-based dynamic models of the compressors and expanders allow calculating internal system variables, such as pressures, temperatures and power, some of which are used as controllable variables. Furthermore, different approximations to model the nonlinear relations between mass flow rate, pressure ratio, and rotor speed in the CAES compressors and expanders, determined by so called “maps”, are proposed based on Neural Networks and physically-based nonlinear functions; these constrain the operation of the turbomachinery, but are usually ignored in existing models. A control strategy for active and reactive power of the CAES system is also proposed. The active power controller allows primary and secondary frequency regulation provision by the turbine and compressor. Special controllers are proposed to restrict the charging and discharging power of the turbine and compressor, to avoid pressure ratios that violate the restriction imposed by the cavern pressure. A surge detection controller for the compressor, and a controller that regulates the inlet temperature at each expansion stage are also presented, and these controls are complemented by a state of charge logic controller that shuts down the compressor or turbine when the cavern is fully charged or runs out of air, respectively. A coordinated droop-based reactive power control is also proposed for the parallel operation of the two synchronous machines, which is used to provide voltage regulation assuming both machines operate synchronized with grid. Finally, the implementation of a block-diagram based CAES model for transient stability studies in the DSATool’s TSAT® software is presented, based on a generic model architecture of the different CAES system's components and their interrelations. The performance of the proposed models, with different levels of detail, are examined in various electrical system studies. First, the potential of a CAES system to provide primary and secondary frequency regulation in a test power system with high penetration of wind generation is evaluated in Simulink®, where the proposed CAES models are also compared with existing models. The voltage regulation, oscillation damping capability, and frequency and transient stability impact of CAES are also studied in a modified WSCC 9-bus test system using TSAT®. It is demonstrated that CAES is more effective than equivalent gas turbines to regulate frequency and voltage and damp low frequency oscillations, with the simultaneous charging and discharging operation significantly reducing the frequency deviation of the system in the case of large power variations in a wind farm. Furthermore, the effects on the overall frequency regulation performance of incorporating detailed models for some of the CAES components, such as expansion air valve, compressor and turbine maps and associated controls is also assessed, demonstrating how modeling these sub systems restricts the CAES response, especially in charging mode. Finally, the effect of the stage of charge control on the frequency stability of the system for different cavern sizes is investigated, concluding that if the power rating of the CAES system is large enough, small cavern sizes may not allow proper provision of frequency regulation.
New Approaches to Composite Reliability Assessment of Smart Power Systems
(University of Waterloo, 2017-05-12) Lami, Badr; Bhattacharya, Kankar
Electric power networks are complex systems because of their geographic spread and the consequent need for interconnections and integration of different components such as generators, transformers, lines, reactors, relays, and loads. Therefore, power utilities seek to ensure an acceptable degree of reliability in planning and operations, and accordingly, need information on component outages while satisfying the growing demand in order to ensure the availability of the system and prevent downtimes. Power systems of today are facing major challenges because of the rapid increase in penetration of energy resources (ERs) and plug-in electric vehicles (PEV). This thesis focuses on the evaluation of composite system reliability using direct probabilistic analysis techniques. The research presents the mathematical foundations, evaluation procedures, and reliability and risk indices associated with composite power system reliability evaluation using the minimal cut set calculations. The concept of minimal cut sets is applied to evaluate two sets of reliability and risk indices, system indices and nodal indices. System indices are essential for system planners and operators to determine the likelihood of interruption of supply, while nodal indices provide useful information on significant load points. The performance of the system under outage condition of generators, transmission lines, or both, is examined by conducting an appropriate power flow study. An optimal power flow (OPF) model is solved to find the system and nodal minimal cut sets and the associated indices. The thesis presents a novel composite system reliability based planning for ERs with clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and Fuzzy C-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an OPF based approach. Once the optimal sizes and locations of ERs are obtained, the earliest year of their penetration into the system is determined using an adequacy check algorithm. The thesis further presents a novel method to detect the critical components of composite power systems under steady-state conditions and short-term operations in order to help planners make economic decisions on new investments in generation capacities and transmission lines upgrades, and also to help operators maintain the delivery of electricity during system failure and disturbance events. Each component is ranked based on minimal cut set outage probability and the consequent loss of load arising from the outages of components belonging to a minimal cut set. Finally, the thesis presents a novel framework to evaluate the impact of PEV charging loads on composite power system reliability. A Smart-OPF model combined with a minimum cut set approach is proposed to evaluate the system reliability indices. Demand response (DR) is included in the proposed procedure and its impact on system reliability indices is studied. The procedure to determine the critical components of the power system in the presence of PEV loads and DR is also proposed.
A Novel Multi-Layer Framework for Dynamic Operation of Prosumers in Peer-to-Peer (P2P) Energy Markets
(University of Waterloo, 2020-12-15) Azam, Muhammad Umar; Bhattacharya, Kankar
Research in transactive energy systems, in recent years, has been primarily focused on the financial aspects of peer-to-peer (P2P) energy trade with little attention paid to the operational and practical aspects of how this energy trade should occur in a system. Practical prosumer behavior in such systems should be subject to their own internal status and the external conditions of the electrical network. Moreover, for such practical realization of a prosumer to be feasible, they should be primed to operate through system disturbances and parameter uncertainties. In this thesis, a novel mathematical model is presented to enable prosumers to partake in P2P energy trades with full operational freedom over their own consumption, energy storage system (ESS) operation and their distributed power generation capability. The proposed model integrates a physical system (physical layer) with prosumer operations (virtual layer) to evolve a multi-layer framework which allows physical network constraints to be implemented with relative ease. The formulation is implemented on a 33-Bus test system considering various system objectives and the results demonstrably prove the significance and applicability of the proposed framework in P2P energy markets. The multi-layer framework is then further extended to enable the prosumer to respond to uncertainties in the local grid or its distributed energy resources, through an MPC based approach. The considered uncertainties are further split into categories termed as ’known uncertainty’ and ’unknown uncertainty’; with the former referring to forecasting errors and the latter referring to unexpected system disturbances. Several cases are developed considering combinations of system parameters to be uncertain and by introducing disturbances to observe prosumer responses. The simulation results prominently show the prosumers responding to unexpected disturbances by adjusting their behavior and P2P energy trade while maintaining their optimal objectives. Such results demonstrate the viability of this MPC approach for the realization of a practical prosumer.