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The bidding behaviors of the energy storage systems (ESS) are complicated due to time coupling and market coupling limited by their capacity states. The existing research is mainly based on optimization models and reinforcement learning (RL) models, which are idealized with analytical objective functions, rational decisions, and virtual historical data. This leads to mismatches between theoretical results and actual bidding behaviors, and the obtained results cannot be generalized to a real-world market. In recent years, the disclosure of market data has enabled data-driven analysis, while the complexity of actual data makes the analysis process challenging and calls for more suitable methods. On the basis of our investigation of ESS bidding behaviors and market data, we propose a novel inverse RL (IRL)-based framework to identify the bidding decision objective function of ESS in coupled multi-market through their historical bidding records and operation status. The identification results can be used as the model parameters to help RL-based simulation models available for a real-world market. Based on the proposed framework, we conduct an empirical analysis on the real-world data of a battery ESS in the Australian electricity market. The results show how the ESS balances its objective among different markets and maintains its state of charge (SOC) to gain more profits. This framework can help us model ESS behaviors by relying on historical data to better understand the ESS bidding decision-making mechanism, which will facilitate market equilibrium analysis and mechanism design. [ABSTRACT FROM AUTHOR]

The massive access to renewable energy sources in smart grids and their inherent uncertainties have led to large fluctuations in the power market's demand margin for conventional electrical energy. Therefore, the stability of the bidding behavior on the power generation side will be affected. To describe the interference of uncertain factors on the bidding process of power generation enterprise groups, we introduce Gaussian white noise into the bidding game. Additionally, taking into account the mutual promotion or mutual inhibition between strategies within the group, we introduce strategy influence factors to improve the replicator dynamics equation. Then, a stochastic evolutionary game model is proposed and solved according to the stability judgment theorem of the stochastic differential equation. The stability and convergence speed of the traditional evolutionary game model and the proposed model are compared in the evolution process of the group strategy. The simulation results show that the strategy with the greater influence is more decisive for the direction of group evolution and the speed of evolution to a stable equilibrium point. Additionally, the proposed model can reflect fluctuations in the evolution process of decision-makers, which promotes the speed with which players evolve toward the optimal strategy. [ABSTRACT FROM AUTHOR]

This paper presents a distribution system operator (DSO) framework for wholesale and retail market participation of distributed energy resources (DERs) aggregators. The DSO coordinates aggregators’ energy and regulation offers as well as end-users’ energy consumption through the unbalanced retail market and submits balanced energy and regulation offers to the wholesale market on behalf of all the aggregators and end-users within its territory. Various kinds of DER aggregators including demand response aggregators (DRAGs), energy storage aggregators (ESAGs), electric vehicle (EV) charging stations (EVCSs), dispatchable distributed generation aggregators (DDGAGs), and renewable energy aggregators (REAGs) are modeled. To handle unbalanced distribution grids with single-phase aggregators, a linearized unbalanced power flow is tailored to model operating constraints of the distribution grid with various aggregators. A market settlement approach is proposed for the DSO, which coordinates with wholesale market clearing process and ensures the DSO’s non-profit characteristic. It is proved that at the wholesale-DSO coupling substation, the total payment received/compensated by the DSO under the wholesale price is identical to that under three single-phase retail prices for each phase at the substation. Case studies are performed on the modified IEEE 33-node and 240-node distribution test systems to investigate the market outcomes of the proposed DSO. [ABSTRACT FROM AUTHOR]

Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world electricity markets. We address this open problem with focus on the electricity market that is operated by the California Independent System Operator (ISO). In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world California ISO market data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the advantages and disadvantages of the existing real-world strategies in order to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used. [ABSTRACT FROM AUTHOR]

Prior-art market mechanism designs in the literature do not well distinguish the primary frequency reserve resources with different response rates. This letter proposes a performance ratio that quantifies the relative effectiveness of generators and flexible demand resources in frequency regulation considering their response rates. The performance ratio is incorporated into a real-time market clearing model to optimize both upward and downward primary frequency reserve resources. The proposed method is validated based on the IEEE 118 bus system. [ABSTRACT FROM AUTHOR]

Due to the deregulation of power systems worldwide, bidding behavior simulation research has gained prominence. One crucial element in these studies is accurately defining and modelling the individual reward function (or objective function). Considering the ubiquitous information barriers between market participants and researchers, the common way is to develop reward functions based on theoretical assumptions, which will inevitably cause deviations from the real world. However, since market data have gradually become transparent in recent years, especially data regarding historical bidding behaviors, it is feasible to introduce data-driven methods to identify the individual reward functions that are hidden in raw bidding data. Thus, this paper proposes a data-driven bidding objective function identification framework with three procedures. First, the bidding decision processes of participants are formulated as a standard Markov decision process. Second, a deep inverse reinforcement learning method that is based on maximum entropy is introduced to identify individual reward functions, whose high-dimensional nonlinearity could be saved in multilayer perceptions (MLPs). Third, a deep Q-network method is customized to simulate the individual bidding behaviors based on the obtained MLP-based objective functions. The effectiveness and feasibility of the proposed framework and methods are tested based on real market data from the Australian electricity market. [ABSTRACT FROM AUTHOR]

We study the equilibria reached in electricity, natural-gas, and carbon-emission markets, where power producers simultaneously participate in these three markets. We use a conjectural-variations equilibrium model to represent both electricity and natural-gas markets, in which power/natural gas-producers play a quantity game and adjust their production decisions in reaction to the production levels of their rivals. We use a cap-and-trade mechanism to model the carbon-emission market. We develop a direct approach to identify equilibria involving the three markets, in which the optimality conditions of all players and market clearing conditions are gathered and solved jointly. Numerical results from two test systems illustrate the impact of carbon-emission trading and transmission constraints on the equilibria reached. Additionally, we compare equilibrium results under different types of competition. [ABSTRACT FROM AUTHOR]

The increasing integration of uncertain and volatile renewable power generation poses challenges to the operation of the interdependent electricity and gas markets. In this paper, a robust operational equilibrium solution method for the interactive markets of power and gas systems is proposed, where the bidirectional interactions include both firm energy and reserve contracting, and the impacts of the uncertainties of wind generation outputs on the two markets are characterized. The line pack as well as bidirectional flow characteristics of gas pipelines are depicted in the gas system model so as to improve its operational flexibility. To guarantee the robustness of market equilibrium against uncertainties, the power and gas market clearing models become two-stage robust ones. Column-and-constraint generation (C&CG) based and the best response algorithms are devised to clear the two markets as well as to coordinate them, respectively. Simulation results validate the effectiveness of the proposed robust operational equilibrium model and the performance of the devised solution methodology. [ABSTRACT FROM AUTHOR]

The structure of the operational costs of gas fired units (GFU) in real power systems is much more complex than what standard unit-commitment (UC) models consider in the state-of-the-art formulations. On the one hand, when a generation company owning several GFUs procures its gas -either totally or partially- at the gas spot market, the final fuel price will depend on the total gas purchases. This fact couples the operation of all the GFUs as it is impossible to build individual cost functions for each generator. On the other hand, as any other gas consumer, the generation company has to pay the gas Third Party Access (TPA) tariffs. The generation company must contract in advance the monthly TPA capacity for each gas exit point and depending on such decision, the final variable cost of the GFUs will be different. This paper states the importance of modeling properly both issues and presents a novel mathematical formulation that can be embedded in a self-UC model. The presented example case highlights the benefits of the proposed formulation. [ABSTRACT FROM AUTHOR]

Traditional cyberattack strategies on the electricity market only consider bypassing bad data detections. However, our analysis shows that experienced market operators can detect abnormal locational marginal prices (LMPs) under the traditional attack model during real-time (RT) operations, because such attack model ignores the characteristics of the LMP itself and leads to price spikes that can be an easy-to-detect signal of abnormality. A detection approach based on the concept of critical load level (CLL) is used to help operators identify risky periods when operators would be prone to overlooking abnormal LMPs. During safe periods, the abnormal LMPs are identified according to the operator's experience, while in risky CLL intervals, a N-x cyber contingency analysis is proposed to help independent system operators (ISOs) detect abnormal LMPs. Further, this paper constructs a new type of cyberattack strategy capable of not only bypassing bad data detection in the state estimation stage but also disguising the compromised LMPs as regular LMPs to avoid market operators’ alerts in a realistic scenario wherein the attacker has imperfect information on system topology. Finally, the proposed analysis method and the attack strategy are evaluated through numerical studies on the PJM 5-bus system and the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

Game theoretic methods and simulations based on reinforcement learning (RL) are often used to analyze electricity market equilibrium. However, the former is limited to a simple market environment with complete information, and difficult to visually reflect the tacit collusion; while the conventional RL algorithm is limited to low-dimensional discrete state and action spaces, and the convergence is unstable. To address the aforementioned problems, this paper adopts deep deterministic policy gradient (DDPG) algorithm to model the bidding strategies of generation companies (GenCos). Simulation experiments, including different settings of GenCo, load and network, demonstrate that the proposed method is more accurate than conventional RL algorithm, and can converge to the Nash equilibrium of complete information even in the incomplete information environment. Moreover, the proposed method can intuitively reflect the different tacit collusion level by quantitatively adjusting GenCos’ patience parameter, which can be an effective means to analyze market strategies. [ABSTRACT FROM AUTHOR]

We study investment equilibria in electricity and gas markets wherein electricity producers and natural gas suppliers behave strategically. We consider also hybrid producers that own both generating units and gas sources. Each strategic producer determines its investment decisions in gas-fired units, and its offering and bidding strategies to maximize its own profit, anticipating electricity and gas market-clearing outcomes. Producers owning gas-fired units submit bids to the gas market to procure fuel and offers to the electricity market to sell electricity. The resulting model is recast as an equilibrium problem with equilibrium constraints that we solve using a direct approach. Numerical results from two test systems illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Constraint relaxation is widely used in electricity market clearing to obtain an acceptable solution when the market model is infeasible facing conflicting operating requirements. However, the penalty factors added in the objective function cause extremely high price spikes. Such a problem is currently handled by ad-hoc manners in power industries. After summarizing the current industrial practice and relative problems, we present a unified pricing model under constraint violations, which considers the prices as decision variables. Theoretical proof shows that the proposed method is capable of representing LMP with price spikes and current industrial practice. Hence, the pricing model has the ability of achieving more desired prices under the same pricing requirements. Furthermore, this paper presents a practical implementation of this unified model. The minimization of uplift payments, which is caused by the split of pricing and scheduling, is used as the objective function. This model can be transformed to a linear formulation without accuracy loss. Case studies show that the proposed pricing method brings distinct advantages compared with current industrial practices. [ABSTRACT FROM AUTHOR]

Flexible ramping products (FRPs) have been implemented by several independent system operators (ISOs) to procure adequate flexible resources. Currently, system operators estimate the system level ramping requirements ignoring the spatiotemporal correlations among various uncertainty sources. This leads to overestimates/underestimates of ramping requirements. In addition, the explicit FRPs model considers only the generation ramping limitation. Other security constraints, such as the transmission limits, are not considered, which leads to deliverability issues of FRPs. To deal with these shortcomings of current FRPs models, this paper proposes a deliverable FRPs based on a distributionally-robust chance constrained multi-interval optimal power flow (DRCC-MIOPF) considering the spatiotemporal correlation of wind power and demand uncertainties endogenously. Furthermore, an asymmetrical affine policy (AAP) is proposed to leverage generation flexibility and mitigate the uncertainty in different directions. The pricing mechanism of the FRPs is proposed using a novel uncertainty-contained locational marginal price (U_LMP) which is derived from the proposed AAP-DRCC-MIOPF model. New components representing the price of FRPs are added into the traditional LMP formulation. Finally, the PJM 5-bus, IEEE 39-bus and IEEE 118-bus systems case studies validate the proposed FRPs approach. The payment of uncertain demand and wind power on FRPs are analyzed. [ABSTRACT FROM AUTHOR]

In major electricity markets, the market clearing model is currently solved in a sequential manner. However, this practice produces uplift payments because the self-dispatch of market participants is calculated over the whole dispatch horizon. In this letter, the uplift payments incurred by the sequential dispatch manner are analyzed. A pricing model that reduces the uplift payments is presented. [ABSTRACT FROM AUTHOR]

Renewable energy will become a normal energy source and will be required to participate in the market. The renewable portfolio standards (RPS) together with tradable green certificates (TGC) are considered as an appropriate market mechanism to help recover renewable energy investments. The strategic offering behaviors of renewable energy should be carefully studied, especially with the introduction of TGC. This paper proposed a market equilibrium model with joint consideration on energy and TGC. The model includes two-stages of day-ahead and real-time analysis, to formulate the complex decision processes of strategic renewable energy. The model is built in a bilevel format, with multiple-scenario settings to consider the uncertainty of renewable power output within a day. Then, the bilevel model is reformulated as a single-level nonlinear problem based on KKT theorems. Then, the nonlinearities are linearized based on the strong duality theorem and a binary expansion method. The model has been finally transformed as a mix integer linear problem. Finally, an illustrative example is proposed to analyze the strategic offering behaviors of renewable energy under various market situations. [ABSTRACT FROM AUTHOR]

This paper develops a systematic approach to derive the optimal bidding strategy for prosumers in a distribution-level energy market. In particular, the optimal bidding curve describes the cost-minimizing buying/selling strategy of prosumers and is composed of a limited number of linear segments. As such, only a small number of end points of the linear segments are sufficient to exactly describe the optimal bidding curve. This not only guarantees the optimality of bidding decisions, but also greatly reduces the computation and communication overhead compared with existing sampling-based approaches. By leveraging the proposed systematic bidding strategy, we propose a novel market clearing scheme for the distribution system operator (DSO) to coordinate the energy transaction among prosumers in a local distribution area (LDA). Specifically, when all prosumers, either grid-connected or isolated, follow the proposed optimal bidding strategy, the DSO is able to achieve a global optimal distributed energy resource (DER) dispatch in a decentralized manner. We verify the proposed bidding strategy and market clearing scheme in two practical scenarios, i.e., a prosumer with 34 buses in a distribution-level energy market and an LDA with four 13-bus microgrids. Numerical results demonstrate the superior performance of our proposed approach compared with other benchmarks. [ABSTRACT FROM AUTHOR]

In power industries and academia, piecewise linearization methods are widely used in optimal power flow (OPF) problems to approximate the nonlinear objective functions. Better computational performance can be achieved. However, limited attention has been paid to the approximation error. In electricity markets, this error could cause distortions of market clearing results and deficits in social welfare. To address this issue, we provide a criterion for the desired number of piecewise segments. We then propose an efficient method for correcting the piecewise linearization error of the DC OPF problems. [ABSTRACT FROM AUTHOR]

Market power has been a problematic issue for fair and transparent trades in various electricity markets. To deal with the problems of existing market power mitigation methods, such as complicated judgment criteria, excessive supervision probability, impersonal cost data accounting, and the requirements on perfect information circumstances, a novel spot market power mitigation clearing mechanism (MPMCM) is proposed, which manages to limit potential market power execution while avoiding overregulation and maintaining a real market supply–demand situation. The MPMCM consists of three processes, namely, market power evaluation, bidding capacity division, and bidding capacity constraint. The MPMCM is transformed into a set of bidding constraints and properly integrated in the common market clearing procedure. A bilevel mathematical problem with equilibrium constraints market equilibrium model is proposed to formulate the MPMCM for market simulation, and the mixed integer linear problem reformulation of the bilevel model is derived. Finally, an illustrative example is proposed to observe the strategic behaviors under various situations, thereby proving the validity and effectiveness of the MPMCM. A large-scale numerical example based on a modified IEEE 200-node system is proposed to prove the adaptivity of the MPMCM. [ABSTRACT FROM AUTHOR]

Due to the increasing penetration of distributed energy resources (DERs), power system operators face significant challenges of ensuring the effective integration of DERs. The virtual power plant (VPP) enables DERs to provide their valuable services by aggregating them and participating in the wholesale market as a single entity. However, the available capacity of VPP depends on its DER outputs, which is time varying and not exactly known when the independent system operator runs the day-ahead unit commitment engine. In this study, we develop a model to evaluate the physical characteristics of the VPP, i.e., its maximum capacity and ramping capabilities, given the uncertainty in wind power output and load consumption. The proposed model is based on a distributionally robust optimization approach that utilizes moment information (e.g., mean and covariance) of the unknown parameter. We reformulate the model as a binary second-order conic program and develop a separation framework to address it. We first solve a two-stage problem and then benchmark it with a multi-stage case. Case studies are conducted to show the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces an empirical approach to dispatch resources in real-time power system operation with growing levels of uncertainties emerging from intermittent and distributed energy resources in the supply and the demand side. It is shown that by taking empirical data of specific sizes, the dispatch results can lead to a quantifiable and rigorous bound on the risk of violating constraints at the implementation stage. In particular, we formulate the look-ahead real-time economic dispatch problem using the scenario approach. This approach takes empirical data as input and guarantees a tunable probability of violating the constraints according to the input data size. By exploiting the structure of the economic dispatch, we show that in the absence of transmission constraints, the number of samples that is required by the theory does not grow with the size of the problem. In the more general case with transmission constraints, it is shown that the posterior bound on the risk of dispatch can be quantified and can be much smaller than the risk bound before solving the dispatch. Numerical examples based on a standard test system suggest that the scenario approach can provide a practically attractive solution with theoretically rigorous properties for risk-limiting power system operations. [ABSTRACT FROM AUTHOR]

Nonconvexity in electricity markets creates difficulties in the pricing of electricity today. Equilibrium prices often do not exist, and some forms of side payments are required to provide revenue adequacy for cleared offers and bids in markets. To improve market efficiency and transparency, academia and industry have proposed different pricing schemes, each of that emphasizes only a certain aspect of the desired price. In this paper, we summarize the existing pricing objectives and propose a unified model for pricing under nonconvexity. The tradeoff among competing objectives can be modeled. A specific example of the unified nonconvexity pricing model is presented, which can be regarded as a general representation of the convex hull pricing method and the commonly adopted locational marginal pricing scheme. The primal formulation of this model is derived. The economic insight of the convex hull pricing method is illustrated based on this model. An extreme-point-based solution method is presented. The proposed pricing model establishes a framework for analyzing the tradeoff among different objectives for pricing under nonconvexity and provides an opportunity to construct more economically justifiable prices through a modification of the objective function and constraints of the proposed pricing model. [ABSTRACT FROM AUTHOR]

A generic constant-efficiency energy flow model is commonly used in techno-economic analyses of grid energy storage systems. In practice, charge and discharge efficiencies of energy storage systems depend on state of charge, temperature, and charge/discharge powers. Furthermore, the operating characteristics of energy storage devices are technology specific. Therefore, generic constant-efficiency energy flow models do not accurately capture the system performance. In this work, we propose to use technology-specific nonlinear energy flow models based on nonlinear operating characteristics of the storage devices. These models are incorporated into an optimization problem to find the optimal market participation of energy storage systems. We develop a dynamic programming method to solve the optimization problem and perform two case studies for maximizing the revenue of a vanadium redox flow battery (VRFB) and a Li-ion battery system in Pennsylvania New Jersey Maryland (PJM) interconnection's energy and frequency regulation markets. [ABSTRACT FROM AUTHOR]

This paper proposes a decision support model to optimize small players’ negotiations in multiple alternative/complementary market opportunities. The proposed model endows players with the ability to maximize their gains in electricity market negotiations. The proposed approach is integrated in a multi-agent simulation platform, which enables experimenting different market configurations, thus facilitating the assessment of the impact of negotiation outcomes in distinct electricity markets. The proposed model is directed to supporting the actions of small players in a transactive energy environment. Therefore, the experimental findings include negotiations in local markets, negotiations through bilateral contracts, and the participation in wholesale markets (through aggregators). The validation is performed using real data from the Iberian market, and results show that by planning market actions considering the expected prices in different market opportunities, small players are able to improve their benefits from market negotiations. [ABSTRACT FROM AUTHOR]

A major restructuring of electricity markets takes place worldwide, pursuing maximum economic efficiency. In most modern electricity markets, including the widely adapted Locational Marginal Price (LMP) market, efficiency is only guaranteed under the assumption of perfect competition. Moreover, market design is heavily focused on deterministic conventional generation. Electricity markets, though, are vulnerable to strategic behaviors and challenged by the increased penetration of renewable energy generation. In this paper, we cope with the aforementioned bottlenecks by investigating the application of Vickrey–Clarke–Groves (VCG) auction in a two-stage stochastic electricity market. The VCG mechanism achieves incentive-compatibility by rewarding market participants for their contribution towards market efficiency, being attractive from both market operation and participants perspectives. Both traditional and VCG market-clearing approaches are explored and compared, investigating as well the impact of increasing wind power penetration. The main shortcoming of VCG, i.e., not ensuring revenue adequacy, is quantified in terms of market budget imbalance for various levels of wind power penetration. To this end, a novel ex-post budget redistribution scheme is proposed, which achieves to partially recover budget deficit. [ABSTRACT FROM AUTHOR]

In this letter, an incentive compatible contract is designed for purchasing energy and ancillary service (AS) simultaneously from strategic frequency regulation service providers (FRSPs) by considering information asymmetries between independent system operator and FRSPs. Here, AS is confined to frequency reserve (FR) for arresting nadir frequency following possible contingencies of the electrical grid. An FRSP has a multi-dimensional private information vector, which determines its operational cost and limitations. Moreover, there is a gaming opportunity for an FRSP that is arisen between energy and AS payments manifesting by manipulation of its bids. Thus, in this letter, a multi-object contract is designed by using the revelation principle to optimally allocate energy and FR among FRSPs. [ABSTRACT FROM AUTHOR]

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case- studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism. [ABSTRACT FROM AUTHOR]

Battery energy storage is becoming an important part of modern power systems. As such, its operation model needs to be integrated in the state-of-the-art market clearing, system operation, and investment models. However, models that commonly represent operation of a large-scale battery energy storage are inaccurate. A major issue is that they ignore the dependence of the charging power on the battery state of energy. Consequently, market players might suffer great monetary losses for not being able to follow their day-ahead schedule and/or deliver the scheduled reserves. In order to bridge the gap between very detailed low-level battery charging constraints and high-level battery operation models used in the literature, this paper examines a dependence of battery charging ability on its state of energy. It proposes a laboratory procedure, which can be used for any battery type and technology, to obtain this dependence. It also formulates an accurate linear battery charging model, which closely approximates the real-life battery charging constraints. The proposed battery charging model is compared against the models commonly used in the literature. Battery operation schedules obtained by all the models are compared against experimentally obtained results in order to assess the value of the proposed model in real life. [ABSTRACT FROM AUTHOR]

Mechanism design is studied for aggregating renewable power producers (RPPs) in a two-settlement power market. Employing an indirect mechanism design framework, a payoff allocation mechanism is derived from the competitive equilibrium of an especially formulated market with transferrable payoff. Given the designed mechanism, the strategic behaviors of the participating RPPs entail a non-cooperative game: It is proven that a unique pure Nash equilibrium exists among the RPPs, for which a closed-form expression is found. Moreover, it is proven that the designed mechanism achieves a number of key desirable properties at the NE: these include efficiency (i.e., an ideal “Price of Anarchy” of one), stability (i.e., “in the core” from a coalitional game theoretic perspective), and no collusion. In addition, it is shown that a set of desirable “ex-post” properties are also achieved by the designed mechanism. Extensive simulations are conducted and corroborate the theoretical results. [ABSTRACT FROM AUTHOR]

In this letter, a new mean-variance optimization-based energy storage scheduling method is proposed with the consideration of both day-ahead (DA) and real-time (RT) energy markets price uncertainties. It considers the locational marginal price (LMP) forecast uncertainties in DA and RT markets. The energy storage arbitrage risk associated with the LMP forecast uncertainty is explicitly modeled through the variance component in the objective function. The quadratic term of this variance is transformed into a second-order cone constraint using the charging and discharging power complementarity of the energy storage system. Finally, the proposed model is formulated as a mixed-integer conic programming problem. Numerical case studies demonstrate the effectiveness of the proposed model for energy storage scheduling considering price uncertainty. [ABSTRACT FROM AUTHOR]

Series FACTS devices are one of the key enablers for very high penetration of renewables due to their capabilities in continuously controlling power flows on transmission lines. This paper proposes a bilevel optimization model to optimally locate variable series reactor and phase shifting transformer in the transmission network considering high penetration of wind power. The upper level problem seeks to minimize the investment cost on series FACTS, the cost of wind power curtailment, and possible load shedding. The lower level problems capture the market clearing under different operating scenarios. Due to the poor scalability of $B\theta$ formulation, the shift factor structure of FACTS allocation is derived. A customized reformulation and decomposition algorithm is designed and implemented to solve the proposed bilevel model with binary variables in both upper and lower levels. Detailed numerical results based on 118-bus system demonstrate the efficient performance of the proposed planning model and the important role of series FACTS for facilitating the integration of wind power. [ABSTRACT FROM AUTHOR]

In this paper, we consider the profit-maximizing demand response of an energy load in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy load, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon Markov decision process (MDP) problem, which requires extremely high computational complexity due to continuous state and action spaces. To tackle the high computational complexity, we propose a dual approximate approach that transforms the MDP problem into a linear programing problem by exploiting the threshold structure of the optimal solution. Then, a row-generation-based solution algorithm is proposed to solve the problem efficiently. We demonstrate through extensive simulations that the proposed method significantly reduces the computational complexity of the optimal MDP problem (linear versus exponential complexity), while incurring marginal performance loss. More interestingly, the proposed demand response strategy hits a triple win. It not only maximizes the profit of the energy load, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed quadratic approximation and improved row generation algorithm increases the energy load's profit by 55.9% and saves the bills of other utilities by 80.2% comparing with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

The demand response provides an opportunity for load serving entities (LSEs) that operate retail electricity markets (REMs) to strategically purchase energy and provide reserves in wholesale electricity markets (WEMs). This paper concerns with the problem of simultaneously determining the optimal energy bids and reserve offers an LSE submits to the WEM as well as the optimal energy and reserve prices it sets in the REM so as to maximize its profit. To this end, we explicitly model the trilayer market structure that consists of a WEM, a REM, and a set of end user customers, so as to capture the coupling between the bidding problem and the pricing problem. Based on the trilayer market model, we then formulate the joint bidding and pricing problem as a bilevel programming problem and further transform it into a single-level mixed integer linear programming problem, which can be solved efficiently. Numerical studies using the IEEE test cases are presented to illustrate the application of the proposed methodology as well as to reveal several interesting characteristics of the LSE's profit-seeking behavior. [ABSTRACT FROM AUTHOR]

Centralized electricity markets currently do not optimize reactive power and voltage in the market clearing software. Voltage and local reliability (VLR) commitment requirements are mostly identified through out of market operational procedures. Failure to maintain VLR may incur voltage collapse, generation, transformer loss, or even blackout. Midcontinent Independent System Operator (MISO) employs binary constraints, minimum/maximum generation constraints, interface constraints, and manual commitments to address VLR requirement in the market clearing process and ensure adequate commitment for reliability. This paper integrates binary VLR constraints in day-ahead security constrained unit commitment to improve market efficiency. However, VLR constraints may cause uplift cost, and sometimes the associated uplift cost can be significant. An uplift cost allocation method with the consideration of resources commitment reasons is developed in this paper. The proposed approach is considered for implementation in MISO market clearing process and a MISO test case is used to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Electricity companies have been under a great pressure to improve their technical and financial performances in order to survive and thrive in the deregulated environment. Therefore, these companies are seeking for innovative approaches to take the most out of the restricted resources. These approaches inherently concentrate the available budgets to the most critical components. In this regard, prioritizing the system components and identifying the most critical ones play a vital role in the planning and operation of the system. This paper presents a novel approach to prioritize power transmission system components. In the proposed methodology, a criticality factor is defined and implemented to rank the components based on their impacts on the operation of the power system. Furthermore, a complete model of substations including misoperation of circuit breakers and protection systems is, for the first time, taken into account in the critical component identification process. The proposed approach is used to identify the vulnerable parts of the system and assist the decision makers to manage the limited resources. The proposed methodology is, then, applied to the IEEE 24-bus Reliability Test System, and components are prioritized accordingly. [ABSTRACT FROM AUTHOR]

This study presents an approach to co-optimization of transactive flexibility, energy, and optimal injection-range of Variable Energy Resource (VER). Flexibility receives immense attention, as it is the essential resource to accommodate VERs in modern power systems. With a novel concept of transactive flexibility, the proposed approach proactively positions the flexible resources and optimizes the demand of flexibility. A surrogate affine approximation (SAA) method is proposed to solve the problem with variable infinite-constraint range in polynomial time. It is shown that SAA is more optimistic than the traditional affine policy in the power literature. The SAA method is also applicable to the search for secure injection-range of VER, which is often heuristically determined in industry given the latest system information. In practice, VER generation beyond the secure injection-range has to be curtailed, even if its cost is lower than the marginal price. The proposed technique helps accommodate more VERs securely and economically by increasing secure injection-range. The model and the solution approach are illustrated in the 6-bus system and IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

This paper addresses the microgrid expansion planning problem. In such a competitive electricity market, it will assist community microgrid (COMG) companies in deciding whether or not they should invest in microgrid installation. A two-stage stochastic optimization approach is proposed to eliminate the traditional centralized planning, which has led to competition among COMGs, generation companies (GENCOs), and transmission companies (TRANSCOs) for power delivery. The objective of the two-stage stochastic programming model is to maximize the expected revenue from these three power companies while ensuring the cost-effectiveness and reliability of the power system under uncertain factors, such as load growth and component outages. The proposed model is solved by decomposing the planning problem into two stages. The goal of the first stage is to maximize the profits of COMGs, GENCOs, and TRANSCOs; the goal of second stage is to minimize short-term operation cost considering uncertainty to enhance the reliability of the system. Computational results from two IEEE test systems are presented to analyze the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The strong volatility of load and intermittent generations may lead to the tension of operating reserve in the modern power system. However, the conventional operating reserve providers, such as coal-fired generating units, are costly and not environment friendly. To address the growing needs for operating reserve, the day-ahead market for operating reserve have been launched in practice, such as Midwest ISO of the US. The high penetration of renewables has contributed to the increasing demand for the flexible dispatch in the generation side. Renewable generation, such as wind power, is being allowed to participate in the markets by providing auxiliary service. Therefore, wind power can be utilized in a positive way by providing operating reserve. A temporal and spatial multi-state model is developed to exploit the potentiality of wind power for providing operating reserve in this paper. First, the multi-state model for different wind farms is formulated based on the predictive distribution of wind power and correlations among different time periods and different wind farms. Second, the upward and downward reserve capacity of wind power is analyzed quantitatively based on the proposed multi-state model. Then, the adjustable reserve capacity of wind power is utilized in a stochastic day-ahead unit commitment (DAUC) model to determine the optimal operating reserve schedule by balancing the generation economy and wind power dispatch margin. The forecast day-ahead expectations and the possible real-time variations of wind power are considered in the proposed DAUC model. Illustrative results demonstrate the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

Demand response (DR) is envisioned as a promising solution to address the demand–supply mismatch issue in electric power systems. A new demand bidding model is considered in this paper to facilitate demand response trading in load curtailment form between the aggregator (buyer) and the large contract capacity customers (seller). Different types of customers with different load curtailment characteristics are considered in the demand bidding, leading to the formulation of a nonlinear optimization problem that is computationally expensive to solve. Delicate mechanisms are incorporated into the proposed purchase optimization scheme to reformulate the nonlinear programming problem into an equivalent linear model that can be solved efficiently. Simulation studies show that the proposed work is promising to minimize the total bidding purchase cost while satisfying the given load curtailment constraints that include the load curtailment and the load curtailment patterns of different customers. [ABSTRACT FROM PUBLISHER]

Recently, we proposed a capacity expansion approach for transmission grids that combines the upgrade of transmission capacity with a transition in system structure to improve grid operation. The key to this concept is a particular hybrid AC/DC transmission grid architecture, which is obtained by uprating selected AC lines via a conversion to HVDC. We have shown that this system structure improves optimal power flow (OPF) solvability and that it can reduce the total generation costs. In this paper, we study the benefits of this hybrid architecture in the context of a deregulated electricity market. We propose an efficient and accurate nodal pricing method based on locational marginal prices (LMPs) that utilizes a second-order cone relaxation of the OPF problem. Applicability of this method requires exactness of the relaxation, which is difficult to obtain for conventional meshed AC transmission grids. We prove that the hybrid architecture ensures applicability if the LMPs do not coincide with certain pathological price profiles, which are shown to be unlikely under normal operating conditions. Using this nodal pricing method, we demonstrate that upgrading to the hybrid architecture can not only increase the effective transmission capacity, but also reduce the separation of nodal markets and improve the utilization of generation. [ABSTRACT FROM AUTHOR]

Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50% price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms. [ABSTRACT FROM PUBLISHER]

A virtual power plant (VPP) is formulated and developed as a service-centric aggregator that enables the market integration of distributed energy resources and simultaneously supports cooperation with the distribution system operator in addressing the issue of network usage. A suitable schedule of interactions and communications between aggregators, market operators, system operators, generators, and consumers, regarding electricity market participation and network operation is proposed and presented in a sequence diagram. The cooperation on congestion management in the distribution network is highlighted as a solution to relieve network constraints via the optimal adjustment of active and reactive power of VPP resources while maximizing renewable energy integration across the pool under management. The VPP reduces uncertainty affiliated with input data by employing the latest forecasts through a rolling horizon approach in the planning stage. Thanks to the flexibility of the VPP to perform rescheduling in accordance with agreements it negotiated with its resources, it becomes possible to refrain from undesirable curtailments. Both the market-integrative and the service-centric roles of the VPP are verified through modeling and simulation with a benchmark European distribution network. The results confirm the added value of the proposed VPP in enhancing the integration of wind and solar power. [ABSTRACT FROM PUBLISHER]

Suitably located energy storage systems are able to collect significant revenue through spatiotemporal arbitrage in congested transmission networks. However, transmission capacity expansion can significantly reduce or eliminate this source of revenue. Investment decisions by merchant storage operators must, therefore, account for the consequences of potential investments in transmission capacity by central planners. This paper presents a tri-level model to co-optimize merchant electrochemical storage siting and sizing with centralized transmission expansion planning. The upper level takes the merchant storage owner's perspective and aims to maximize the lifetime profits of the storage, while ensuring a given rate of return on investments. The middle level optimizes centralized decisions about transmission expansion. The lower level simulates market clearing. The proposed model is recast as a bi-level equivalent, which is solved using the column-and-constraint generation technique. A case study based on a 240-bus, 448-line testbed of the Western Electricity Coordinating Council interconnection demonstrates the usefulness of the proposed tri-level model. [ABSTRACT FROM PUBLISHER]