Search

Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large‐scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small‐scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long‐distance transmission. This paper proposes a deep reinforcement learning (DRL)‐based energy management system (EMS) called DRL‐MG to process and schedule energy purchase requests from customers in real‐time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q‐learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real‐time scheduling methods significantly. [ABSTRACT FROM AUTHOR]

Following Part I of this paper that reveals two distinct interpretations of demand curves and the corresponding different forms of the market clearing formulation, Part II of this paper analyzes the sequential and the iterative market clearing algorithms currently implemented in some markets. It is shown that these algorithms lead to socially sub-optimal solutions. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is then proposed. A small numerical example is used to demonstrate the significant optimality gap between the socially optimal solution and the sequential/iterative solution. The potentially large impacts of the gap on the societal benefit and the individual resource revenues entail a close examination of these algorithms. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of discrete secondary controllers on the dynamic response of power systems. The idea of the paper originates from the observation that there is a range of values, from few tens of seconds to few minutes, of the execution cycles of conventional automatic generation control (AGC) that leads to a limit cycle. Below and above this range the system is stable. This is certainly not a problem in practice as the AGC updates the power set points of generating units every few seconds. However, this phenomenon has interesting consequences if one considers real-time electricity markets with short dispatch periods (i.e., 5 minutes) as these markets can be modeled as a sort of AGC. The paper first provides a formal analogy between conventional AGC and real-time electricity markets. Then it shows that the discretization-driven instability exists if the system includes a real-time electricity market modeled as secondary frequency controller. Finally, the paper discusses the impact of the combined effect of high wind generation shares and discrete secondary controllers on power system dynamics. [ABSTRACT FROM AUTHOR]

The presence of non-convexities in electricity markets has been an active research area for about two decades. The — inevitable under current marginal cost pricing — problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method — i.e., yielding exact, not approximate, CH prices — with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept. [ABSTRACT FROM AUTHOR]

In the absence of demand-side bids for certain reliability products in the wholesale electricity markets, Independent System Operators (ISOs) traditionally use fixed demand requirements with penalty factors to clear the market. This approach does not allow proper tradeoffs between cost and reliability due to the inelasticity of the fixed requirements. Therefore, ISOs have been replacing the fixed requirements with sloped demand curves. However, the economic interpretation of demand curves, especially for multiple coupled products, is largely missing in both theory and practice. This could lead to the misrepresentation of the demand benefit in the market clearing objective. Using a two-product market model, Part I of this paper reveals two distinct interpretations of demand curves, each associated with a specific form of the market clearing problem. This implies that the construction of demand curves and the use of them in market clearing must be consistent with their economic interpretation. In Part II of this paper, we analyze the sequential and iterative market clearing processes currently implemented in some markets to demonstrate their solution sub-optimality. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is proposed. [ABSTRACT FROM AUTHOR]

Near-optimal unit commitment (UC) scheduling is a practical reality in wholesale electricity markets. This paper revisits previous work that has found that minor differences in cost among near-optimal schedules can result in a large redistribution of market payments (i.e., changes in generator profits and consumer surplus). It has been believed that this instability is unavoidable, but previous studies have only calculated prices using what we call the Restricted pricing model. This paper compares previous results to three additional models that are based on integer relaxation, and which we call the Partial, Tight, and Loose Dispatchable pricing models. Results are presented for a suite of test cases, including four ISO-scale cases. Similar to previous findings, the Restricted and Partial Dispatchable models both result in large payment redistributions among alternative solutions. In contrast, theoretical and experimental results for the Tight and Loose Dispatchable models show that pricing models with unconditional integer relaxation will have bounded payment redistributions, and, further, this bound can become quite small by tightening the UC problem's convex relaxation. In the presence of market power, stable financial outcomes may improve market efficiency by reducing incentives to bid strategically. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-analytical method to obtain the equilibrium bidding strategies for generation and demand units in a combined oligopoly and oligopsony wholesale electricity market. Such market structure is the outcome of the increasing deployment of demand response programs that facilitate active participation of demand-side players in the price-setting process. In this analysis, the concept of supply function equilibrium (SFE) is used to investigate the oligopolistic competition among generation units. The SFE model is extended and the demand function equilibrium (DFE) is obtained to study the oligopsonistic competition among demand units. The economic behavior of a market participant is formulated as a bi-level programming (BLP) problem. The imperfect competition among generation units, as well as among demand units, are modeled as a non-cooperative game. Next, a direct method is developed to calculate all candidate equilibriums of the market, and the locational marginal prices (LMPs) in terms of the bidding strategies of the market participants. The BLP problem is solved by obtaining the coordinated Pareto-dominant Nash equilibrium of the market participants’ non-cooperative games. Finally, the proposed analysis is examined in case studies. Accordingly, we report insightful observations with respect to the impact of the changes in the new market structure, at firm-level and market-level, such as in terms of mitigating market power of generation units, the market clearing prices and quantities, surplus for generation units and demand units, and potential impact on market efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a market framework to approach economic dispatch as a subproblem in smart distribution systems. Four subproblems are associated with four different submarkets (SMs) in the presented framework. Market participants are eligible to bid in different submarkets based on the type of their distributed generation (DG) unit. To effectively tackle the conflicts among objective functions of different submarkets, the corresponding formulated subproblems are assigned to two different agents, namely distribution system operator (DSO) and a green bank agent (GBA). A communication mechanism is designed within the proposed framework such that DSO and GBA compromise their solutions and find a tradeoff to reasonably address the concerns of the four subproblems within SMs 1-4. The effectiveness of the proposed market framework is validated on a well-known 136-bus distribution system, which is modified to contain wind turbine DG aggregators, photovoltaic DG aggregators, and diesel-engine DG aggregators. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing, and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse, and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system, and comment on the impacts of both proactive, and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

Block orders are increasingly adopted in many electricity markets as they enable power producers to implicitly express their unit commitment characteristics and address price volatility risks. However, previous work on the optimal offering problem of a price-taking producer participating in such markets has only considered the simplest type of block orders (regular block orders-RBO) and has neglected relevant realistic market regulations. This paper addresses these simplifications by proposing a new optimal offering model which also considers the more complex profile block orders (PBO) and linked block orders (LBO). This model is generic in that it can treat the block orders’ time spans either as fixed parameters (following the modelling approach of previous work) or as endogenous decision variables, which constitutes a completely novel modeling approach. The presented case studies demonstrate that this novel approach yields a tremendously lower number of binary variables and more efficient computational performance, when PBO and LBO are considered. Furthermore, the case studies analyze the physical conditions under which the introduction of different types of block orders increases the examined producer’s profit. [ABSTRACT FROM AUTHOR]

Over the past 20 years, the rapid integration of renewable energy has resulted in electricity markets that are increasingly complex, interconnected, and uncertain. Similar to the banking system's financial crisis in 2008 due to chained reactions, severe financial losses due to uncertainty at a large renewable farm could induce significant financial losses at other market participants. The spread of such financial shocks can be worsened by centralized market clearing, and systemic risks consequently germinate in electricity market operations. Therefore, our previous work has compared systemic risk with systematic risk in the electricity market with uncertainties. However, the scope of our previous work has been limited to risk indices. This paper aims to broaden the study by proposing the theoretical foundation of systemic risk analysis in electricity markets with uncertainties. First, an electricity market financial network is defined to describe the cash inflow/outflow of all participants, and the financial contagion is employed to model the interlinks between different entities. Then, two financial properties, financial resilience and financial reliability, are proposed to evaluate the systemic risk in market settlements with uncertainties. Finally, the proposed theoretical foundation of systemic risk is demonstrated on the Texas synthetic 2000-bus system with 70% renewable penetration. [ABSTRACT FROM AUTHOR]

Smart buildings form an integral part of the smart grid that lowers electricity cost. This objective can be achieved by using suitable enhanced algorithms and techniques. The motivation of this paper is the request for an economical decision-making system from a commercial company in Singapore. This paper presents an economical building management system (EBMS) concept inspired by the building management system and machine learning. EBMS improves the decision-making process through rule-based algorithm within the system. Machine learning algorithm is included as part of EBMS to enhance the computational calculation for the electricity distribution. It includes the function of self-making decision for purchasing electricity based on forecasting system for electricity price or real-time electricity price data when facility managers are not present. EBMS uses multiagent system to communicate, interact, and negotiate with multiple agents for energy supply and demand in the building. Simulation studies have shown the potential of EBMS concept to provide a cost reduction solution up to 61.42% savings for smart buildings. Better electricity purchasing contract option can be decided based on the forecasting system for building power consumption. [ABSTRACT FROM AUTHOR]

In this paper, we present theory and a design of the online double auction for the trading of energy within a smart grid with microgrids (MGs). The online double auction has the potential to enable the allocation of surplus electricity to the MGs that need electricity with the highest gain in the real-time market. Nonetheless, two critical issues remain challenging when designing an effective online double auction scheme in such a system. First, as the agents are allowed to arrive and depart at any time, the auctioneer needs to make decisions without the information of further bids and asks. Second, the economic properties of strategy-proof, individual rational, and (weak) budget balance should be satisfied. To address these issues and enable multiunit electricity trading among local MGs, in this paper, we propose a strategy-proof online double auction (SODA) scheme, in which the surplus and insufficient MGs in the system are treated as sellers and buyers, respectively, and the MG center controller is capable of maximizing the social welfare of MGs by appropriately matching buyers and sellers. Via theoretical analysis, we prove that SODA can achieve the properties of individual rationality, (weak) budget balance, strategy-proofness, and computational efficiency. Experiments also show that SODA is capable of reducing the energy purchasing cost of the MGs and shifting the peak-load, while achieving great performance with respect to social welfare, seller/buyer satisfaction ratio, social efficiency, and computation overhead. [ABSTRACT FROM AUTHOR]

Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ahead and intraday dispatch model for maximizing the profit of an Integrated Biomass-Concentrated Solar (IBCS) system considering the synergies arising from their coupled operation. To sensibly capture uncertainty and decision sequence of real-life electricity markets, a two-stage stochastic structure is proposed, while the solar-related uncertainty is involved using Information Gap Decision Theory (IGDT). The model is complemented with a novel multi-objective architecture based on the compound of IGDT and Conditional Value-at-Risk (CVaR), which allows handling risk exposure to both stochastic and IGDT inputs. The Pareto strategies in the multi-objective model are extracted through an expanded form of the $\epsilon$ -constraint method, whereas a posteriori approach based upon the out-of-sample assessment is applied to derive the optimal dispatch pattern among the generated Pareto strategies. The simulation results demonstrate that: 1) the proposed integrated dispatch model achieves substantial profitability, and 2) the performance of the suggested CVaR-IGDT model is superior to conventional approaches. [ABSTRACT FROM AUTHOR]

Wind power producers participating in day-ahead electricity markets are compelled to pay imbalance costs if they do not generate the same amount of power as they had bid for. These imbalance costs comprise a significant proportion of their income. To reduce the risk of such financial losses, this paper employs the idea of trading in a separate prediction market, as a hedging method. In prediction markets, participants trade shares associated with a certain outcome of an event. We propose that the wind power producers might participate in a prediction market to trade the future value of the wind power and by taking an opposite position in comparison to the electricity market, the imbalance costs will be offset through payouts in the prediction market. Wind power is modelled as a stochastic variable and an optimal trading strategy is developed where the trading volume in the prediction market is analytically derived and formulated by minimising the maximum possible loss and pricing of shares is determined via indifference utility condition. The results suggest that the proposed method limits the loss values and improves the risk measures. [ABSTRACT FROM AUTHOR]

This paper develops a new electricity market clearing mechanism, called risk-based locational marginal pricing (RLMP). The RLMP is derived from the risk-based security-constrained economic dispatch model, where risk is modeled to capture the system's overall security level. Compared to the traditional locational marginal pricing (LMP) which is composed of three components: energy, congestion, and loss, the RLMP consists of an additional risk component. The risk component is a price signal to reflect the system's overall security level. This paper discusses the features of RLMP, and analyzes its benefits over traditional LMPs. The proposed concept and method are illustrated on a six-bus system and the WECC 240-bus system. [ABSTRACT FROM PUBLISHER]

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]

Energy storage (ES) is playing a vital role in providing multiple services in several electricity markets. However, the benefits and risks vary across markets and time, which justifies the importance to optimize ES capacity share in different markets. In this paper, a novel portfolio theory-based approach is proposed for optimally managing ES in various markets to maximize benefits and reduce the risk for ES owners. Three markets are considered, which are energy arbitrage, ancillary services, and distributed network operator's market. They are modeled based on energy cost, frequency response cost, and system congestion cost. Portfolio theory is utilized to quantify ES capacity allocated to each market over time for various levels of risk aversions. The relation between risks and expected return of different markets is efficiently reflected by the portfolio theory, providing implications to storage operation. The extensive demonstration illustrates that the markets that storage can participate in are fundamentally different regarding to its risk aversion. In addition, the optimum portfolio of the markets for storage is on the efficient frontier, providing the maximum return at a certain risk aversion level. This study is particularly useful for guiding market participation and operation of ES to gain maximum economic return at minimum risk. [ABSTRACT FROM AUTHOR]

In the restructured power industry, bulk energy storage may play a crucial role to provide the flexibility required by system operators to cater for the unprecedented levels of uncertainty. Within the context of co-optimized electricity markets for energy and reserves under wind uncertainty, this paper addresses the incorporation of bulk energy storage units in day-ahead network-constrained energy and reserve scheduling. A novel two-stage robust optimization approach is presented whereby the nonconvex and time-coupled operation of storage devices is precisely modeled while accounting for the anticipativity of the two-stage setting. The resulting robust counterpart is cast as a mixed-integer trilevel program with lower-level binary variables. In order to address the nonconvexity of the recourse problem, this paper proposes the application of an exact nested column-and-constraint generation algorithm. Numerical results illustrate the effective performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The mushrooming of distributed energy resources turns end-users from passive price-takers to active market-participants. To manage massive proactive end-users efficiently, virtual power plant (VPP) as an innovative concept emerges. It can provide some necessary information to help consumers improve their profits and trade with the electricity market on behalf of them. One important information desired by consumers is the prediction of renewable outputs inside this VPP. Presently, most VPPs run in a centralized manner, which means the VPP predicts the outputs of all the renewable sources it manages and provides the predictions to every consumer who buys this information. We prove that providing predictions can boost social total surplus. However, with more consumers and renewables in the market, this centralized scheme needs extensive data communication and may jeopardize the privacy of individual stakeholders. In this paper, we propose a decentralized prediction provision algorithm in which consumers from each subregion only buy local predictions and exchange information with the VPP. Convergence is proved under a mild condition, and the demand gap between centralized and decentralized schemes is proved to have zero expectation and bounded variance. Illustrative examples show that the variance of this gap decreases with more consumers and higher uncertainty. [ABSTRACT FROM AUTHOR]

Convergence bidding (CB), a.k.a., virtual bidding (VB), is a market mechanism facilitated by Independent System Operators (ISOs) in wholesale electricity markets to help lower the gap between the prices in the day-ahead market (DAM) and prices in the real-time market (RTM). In this paper, we seek to answer two questions: 1) how can a strategic market participant maximize its profit when submitting CBs? 2) how can such strategically placed CBs affect the price gaps? Answering these questions is not straightforward but the results are insightful. The bidding problem in this context is a bi-level optimization, where the upper-level is about maximizing the profit for the convergence bidder and the lower-level is the economic dispatch problem. By solving the formulated bidding problem, we investigate the impact of strategic CBs on the DAM and RTM locational marginal prices (LMPs) under various practical scenarios. We demonstrate the scenarios under which a strategic CB, whether on its own, or when it is submitted jointly with a physical demand or physical supply bid, can or cannot work as intended, and result in decreasing or increasing the price gap in nodal electricity markets. We also examine how the performance of strategic CBs can be affected by uncertainty in demand or generation bids as well as physical contingencies in the power system, such as transmission line tripping. Special cases, such as net-zero convergence bidding are studied. The long-term performance of strategic convergence bidding is investigated. The above and other market implications are discussed in multiple case studies. [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]

The coordinated multi-area economic dispatch enables optimal resource utilization in a larger spatial range. To overcome drawbacks such as heavy communication burden, convergence failure, and weak scalability of iterative coordination methods, this paper proposes a fully non-iterative multi-area coordination framework. The proposed framework is based on a novel system reduction technique termed as the condensed system representation (CSR). The CSR makes external equivalence of the dispatch problem of each area by exploiting power system operating characteristics, i.e., only a small number of generators may become marginal units and only a minority of security constraints may be active. An algorithm based on the optimality condition analysis is developed to identify the CSR by fixing non-marginal units to their output bounds, eliminating redundant security constraints, and making Norton equivalence of the internal network. With CSRs submitted by local areas, the multi-area system can be optimized without iterative information exchange. Case studies based on a 3-area 9-bus system verify the effectiveness of the CSR-based coordination framework. Larger-scale test systems are constructed to validate the computational efficiency, robustness, and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, three single-stage stochastic programs are proposed and compared for optimal dispatch by a System Operator (SO) into balancing markets (BM).The motivation for the models is to represent a possible requirement to undertake system balancing with increasing amounts of intermittent renewable generation. The proposed optimization models are reformulated as tractable Mixed Integer Linear Programs (MILPs) and these consider both fuel cost and intermittency cost of the generators, when the SO activates the up- or down-regulation bids. These three models are based on the main approaches seen in practice: dual-imbalance pricing, single imbalance pricing and single imbalance pricing with spot reversion. A scenario-generation algorithm based on predictive conditional dynamic density distributions is also proposed. We perform a comparative analysis of these three proposed models in terms of how they help the SO to optimize their balancing market actions considering intermittent-renewable generators. The single imbalance pricing is found to be the most market efficient. [ABSTRACT FROM AUTHOR]

The design of electricity imbalance pricing mechanisms is internationally controversial. Policies on whether to permit virtual trading for market participants and whether, or how, to impose penalty incentives on the imbalance volumes vary widely. Furthermore, market designs vary depending whether the imbalance prices are obtained directly from real-time trading or based upon the offer and demand functions from the day-ahead energy markets. This paper develops an analytical framework for evaluating designs for imbalance settlement mechanisms and we have selected the Japanese electricity market, which has undergone several revisions in its imbalance mechanism, as a good example to assess such variations. We develop a predictive approach for the imbalance volumes and price densities using two-step quantile regressions and derive a new trading optimization for a virtual trader's arbitrage position. We construct supporting models to estimate prediction errors for renewable power and demand as drivers of imbalance volume. The empirical analysis reveals that even in a mechanism with imbalance penalties based upon day-ahead reference prices, virtual trading may still be beneficial to market participants as well as to the system operator. We also find that greater market transparency is crucial for increased benefits. The insights generalize beyond the Japan case study. [ABSTRACT FROM AUTHOR]

Due to deforestation, greenhouse gases and increasing air conditioner systems, the temperatures are changing drastically and the effects are prominent in summer and winter seasons. These ambient temperature variations are affecting the transmission system parameters and also the voltages at various buses. Changes in the transmission line parameters have non-negligible impact on system performance and present Energy Management applications are not considering these temperature variations in the power system state estimation. In this paper, impact of temperature variations on the power system Voltage Stability assessment is considered. Voltage stability assessment methods make use of the load flow results. Load flow results without considering the temperature variations are inaccurate in the real-time and leading to the erroneous results in the voltage stability assessment. In view of this, studies have been carried out on IEEE 30-bus, 118-bus and 300-bus systems, incorporating the seasonal temperature variations on various types of conductors, and also demonstrated the impact with conventional and ZIP type load models. In this paper, four voltage stability indices NLVSI, $L_{mn}$Lmn , FVSI and VCPI(power) are used for identifying the proximity of voltage collapse point, critical line, critical bus voltage, critical bus angle, total system active power loss, total system reactive power loss etc. [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]

Determining contingency aware dispatch decisions by solving a security-constrained optimal power flow (SCOPF) is challenging for real-world power systems, as the high problem dimensionality often leads to impractical computational requirements. This problem becomes more severe when the SCOPF has to be solved not only for a single instance, but for multiple periods, e.g. in the context of electricity market analyses. This paper proposes an algorithm that identifies the minimal set of constraints that exactly define the space of feasible nodal injections for a given network and contingency scenarios. By internalizing the technical limits of the nodal injections and enforcing a minimal worst-case impact of contingencies to line flows, computational effort can be further improved. The case study applies and analyzes the methods on the IEEE 118 and A&M 2000 bus systems, as well as the German and European transmission systems. In all tested cases the proposed algorithm identifies at least 95% of the network and security constraints as redundant, leading to significant SCOPF solve time reductions. Scalability and practical implementation are explicitly discussed. The code and input data of the case study is published supplementary to the paper under an open-source license. [ABSTRACT FROM AUTHOR]

Inverse equilibrium modeling fits parameters of an equilibrium model to observations. This allows investigation of whether market structures fit observed outcomes and it has predictive power. We introduce a methodology that leverages relaxed stationarity conditions from Karush-Kuhn-Tucker conditions to set up inverse equilibrium problems. This facilitates reframing of existing equilibrium approaches on power systems into inverse equilibrium programs. We illustrate the methodology on network-constrained and unconstrained Nash-Cournot games between price-making power generators. The inverse equilibrium problems in this paper reformulate into linear programming problems that are flexible and interpretable. Still, inverse equilibrium modeling provides generally inconsistent estimation and econometric approaches are better for this purpose. [ABSTRACT FROM AUTHOR]

A basic principle of electricity market design is that cost allocation should follow cost causation as closely as possible. As renewable resources play a larger role in electricity generation, system operators have become increasingly concerned about costs related to variability in net load. While variable generation resources have attracted a great deal of attention along these lines, load is a larger source of variability in most systems. This paper introduces a Shapley value framework to assess the total system cost attributable to electricity market participants that either exacerbate or alleviate net load variability. Furthermore, this paper investigates the extent to which these costs are adequately reflected in wholesale market prices. Numerical tests indicate that the cost of variability on the hourly time scale is in most cases internalized by market-integrated generators and buyers, mitigating concerns that variability imposes a socialized cost from ramping or cycling of thermal generators. [ABSTRACT FROM PUBLISHER]

This paper develops an equilibrium-based model for ex ante analysis of capacity withholding under financial virtual divestiture in a day-ahead market, including one dominant producer and a number of small producers considered as a price-taking competitive fringe. We model the offering problem of the dominant producer as a bilevel optimization problem. In the upper-level problem, the dominant producer derives optimal strategy of physical capacity withholding restricted by operational limits of its own generating units and conditions stated in the financial virtual divestiture. In the lower-level problem, market operator clears the day-ahead market on an hourly basis. We recast the bilevel optimization problem as a mathematical programming with equilibrium constraints problem and convert it to a mixed integer linear programming problem. We show how the application of the financial virtual divestiture may mitigate market power in the day-ahead market, through a case study. The numerical results demonstrate that the dominant producer can exploit technological diversification of its own generating units to exercise market power, even in the presence of this market power mitigation policy. [ABSTRACT FROM PUBLISHER]

The increasing penetration of variable energy resources has led to more uncertainties in power systems. Flexible Ramping Products (FRP) have been adopted by several electricity markets to manage the uncertainties. We reveal that neglected line congestion for FRP may not only cause infeasibility, but also result in a failure of cost recovery. To address the deliverability issues on FRP, this paper proposes a new concept, Deliverable Robust Ramping Products (DRRP), in real-time markets. The DRRP includes generation ramping reserve and generation capacity reserve. The DRRP is deliverable and immunized against any predefined uncertainty. It also fully addresses the bid cost recovery issue caused by the line congestion in existing FRPs. The prices of DRRP are derived within the Affine Adjustable Robust Optimization (AARO) framework. These prices can be used to identify valuable reserves among available reserves and quantify the values of flexible resources that provide reserves. This paper also proposes a general approach to obtaining the time-decoupled prices for DRRP and generation, which can be used for the market settlement of the first interval only in real-time markets. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of DRRP and the advantages of DRRP compared to existing FRP. [ABSTRACT FROM PUBLISHER]

Time-shiftable loads have recently received an increasing attention due to their role in creating load flexibility and enhancing demand response and peak-load shaving programs. In this paper, we seek to answer the following question: how can a time-shiftable load, that itself may comprise of several smaller time-shiftable subloads, submit its demand bids to the day-ahead and real-time markets so as to minimize its energy procurement cost? Answering this question is challenging because of the inter-temporal dependencies in choosing the demand bids for time-shiftable loads and due to the coupling between demand bid selection and time-shiftable load scheduling problems. Nevertheless, we answer the above question for different practical bidding scenarios and based on different statistical characteristics of practical market prices. In all cases, closed-form solutions are obtained for the optimal choices of the price and energy bids. The bidding performance is then evaluated in details by examining several case studies and analyzing actual market price data. [ABSTRACT FROM PUBLISHER]

Carbon financing policies such as emission trading have been used to assist in emission mitigation worldwide. As energy end-users/consumers are the underlying driver of emissions, it would be difficult to effectively mitigate carbon emissions by creating an emission trading market without active end-users’ involvement. In electricity markets, demand side management (DSM) in the smart grid can manage demands in response to power supply conditions and influence end-users to contribute to improving both network efficiency and economic efficiency. However, it is a relatively new topic to study the environmental benefits of DSM. This paper proposes a two-stage scheduling model to comprehensively investigate the environmental benefits of consumers participating in both electricity and carbon emission trading markets through active DSM. A developed zero sum gains-data envelopment analysis (ZSG-DEA) model based multi-criteria allocation scheme for emission allocation is employed. Meanwhile, the carbon emission flow model (CEF) is applied to track the “virtual” carbon flow accompanying power flow. According to case studies on the IEEE 24-bus system and IEEE 118-bus system, the proposed model can effectively achieve carbon emission mitigation and provide consumers extra environmental benefits in some scenarios. This model can be an important guide for governments to establish emission trading schemes. [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]

This paper studies the convex hull pricing problem in electricity markets using a network-flow-based formulation. The network represents the feasible operating region of a generating unit, and the associated flow constraints define a polyhedron with an integrality property. These facts provide modeling flexibility with respect to the inclusion of unit features and allow to obtain convex hull prices from a linear programming problem. The formulation is solved using a primal-dual approach based on the algorithm developed by Bienstock and Zuckerberg. The algorithm, together with the implemented pre-processing and initialization techniques, allows achieving lower solution times than those obtained by state-of-the-art algorithms available in commercial solvers, e.g., barrier and dual simplex. Furthermore, results suggest that the proposed formulation obtains the minimum uplift payments even when time-dependent start-up costs are included, making the approach more robust than the best documented compact formulation. The paper also discusses the effect of sub-optimal prices on uplift payments by relaxing the optimality criterion of the algorithm, observing a significant impact on lost opportunity costs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for modeling and revealing the competitors' behavior from perspective of an intended player (IP). To this end, from perspective of IP, we define an Equivalent Rival (ER) whose behavior in the electricity market reflects the aggregation of behaviors of all individual competitors. It is assumed that IP and its ER participate in an equivalent market which its outcomes are approximately equal to those of the real market. The revealing procedure is designed as a two‐stage Artificial Neural Network‐based approach to estimate and predict the bids of ER after each run of the real market. Predicted bids of ER are used for the bidding strategy of IP. The proposed approach has been examined on two different case studies. In the first case study the aggregate supply curve of a market with 12 players has been obtained using the proposed approach and the result has been compared with a Bayesian inference approach. In the second case study a six‐player electricity market is considered. The competitors' behavior has been revealed from perspective of an intended player using proposed approach and an optimal bidding strategy based on the proposed approach has been constructed. [ABSTRACT FROM AUTHOR]

The appearance of the flexible behavior of end-users based on demand response programs makes the power distribution grids more active. Thus, electricity market participants in the bottom layer of the power system, wish to be involved in the decision-making process related to local energy management problems, increasing the efficiency of the energy trade in distribution networks. This paper proposes monopolistic and game-based approaches for the management of energy flexibility through end-users, aggregators, and the Distribution System Operator (DSO) which are defined as agents in the power distribution system. Besides, a 33-bus distribution network is considered to evaluate the performance of our proposed approaches for energy flexibility management model based on impact of flexibility behaviors of end-users and aggregators in the distribution network. According to the simulation results, it is concluded that although the monopolistic approach could be profitable for all agents in the distribution network, the game-based approach is not profitable for end-users. [ABSTRACT FROM AUTHOR]

Successful trading in electricity markets relies on the market actor's ability to accurately forecast the electricity price. The fundamental electricity price models use market information, provided by various price drivers, including the residual that contains a risk premium. In the past, researchers investigating risk premium focused primarily on daily spot price levels, ignoring the intraday information hindering the accurate risk premium determination. This paper presents a new KGB Method for modelling of risk premium, based on “ex-ante” approach focused on a yearly product. The method involves a novel KGB Model and its linearized formulation, the KGB Linear Model, which enables capturing the influence of renewable energy sources on risk premium. The four key drivers of the KGB Linear Model were used providing an insight into the influence of RES generation on risk premium evolution. The method was tested on historical data from the German electricity market. The results for the 2010–2014 period reveal overall influence of PV production share on risk premium is greater than that of wind production share, both increasing the risk premium due to their variability and uncertainty. Using the KGB Method, market actors can forecast risk premium using information readily available to them. [ABSTRACT FROM AUTHOR]

This paper proposes a novel structure for industrial demand response aggregators (IDRAs) to provide operational flexibility for the power system. A robust self-scheduling approach is formulated for the first time to optimize different sub-processes of the whole production line of heavy industries. The new approach satisfies the customer order with the lowest energy cost. Numerical studies are implemented on eight integrated cement factories, from Khorasan Regional Electric Company, in the east of Iran. The results show that the integrated model of heavy industries provides guaranteed flexibility to the system when a power shortage occurs or system reliability is jeopardized. [ABSTRACT FROM AUTHOR]

In mid-term planning, the objective of an electricity retailer is to procure the electricity energy to supply its demands, while efficient control of the financial risks under uncertainties is as vital as maximizing profit. To meet these challenges, this paper proposes and compares different stochastic-based risk measures including minimax regret (MMR), chance-constrained (CC), and conditional value-at-risk (CVaR) criteria, which are useful tools for assessing the risk levels, considering whether a retailer is a risk-taking or a risk-averse participant. To obtain an optimal procurement strategy, robust versions of implemented approaches are applied. To show the real performance of the proposed procurement solutions, postoptimization procedures with different price behaviors are performed to carry out experimental results on the basis of realistic data. The results show that for the same robustness degree of strategy, CVaR is less conservative than MMR and CC is less conservative than CVaR. [ABSTRACT FROM AUTHOR]

In this paper we present the dilemma dealing with the choice of speech segment duration and the calculation of speech signal quality in the packet telephone network of Electricity supply industry (ESI). The analyzed signal is packetized by one packet. The characteristic of this network is that disturbances of long duration in power system produce burst packet loss. The longer speech segments increase the packet delay, but decrease the number of lost packets in one burst, and vice versa. Here we present why it is better to choose speech segments of short duration. Also, we suggest the corrected method for the calculation of speech signal quality in packet network under the influence of long duration disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes a peer-to-peer energy market platform based on the new concept of multiclass energy management, to coordinate trading between prosumers with heterogeneous (i.e., beyond purely financial) preferences. Power networks are undergoing a fundamental transition, with traditionally passive distribution network consumers becoming “prosumers”; proactive consumers that actively manage their production and consumption of energy. The paper introduces the new concept of energy classes, allowing energy to be treated as a heterogeneous product, based on attributes of its source, which are perceived by prosumers to have value. Examples include generation technology, location in the network and owner's reputation. The proposed peer-to-peer energy market platform coordinates trading between subscribed prosumers and the wholesale electricity market, to minimize costs associated with losses and battery depreciation, while providing added value by accounting for the prosumers’ individual preferences for the source/destination of the energy they consume/produce. The decomposable structure of the multiclass energy management problem is exploited to devise a distributed price-directed optimization mechanism, providing scalability and prosumer data privacy. Receding horizon model predictive control allows the prosumers to adjust their planned power flows based on the wholesale energy price, and up-to-date renewable generation and load predictions. [ABSTRACT FROM AUTHOR]

The paper presents the design and analysis of an un-regulated isolated DC–DC converter using wide band gap (WBG) devices for low-voltage, high current applications. Un-regulated isolated DC–DC converter uses a passive rectifier stage at the front end to connect to the utility grid. This will have a voltage stress of 480–780 V DC at the input of the isolated DC–DC converter. For the evaluation, SiC devices are used at the primary side and GaN devices at the secondary side of the converter. Later on the conclusion, a regulated isolated DC–DC converter, which uses an active PFC at the front-end, will be recommended for having less chip area utilisation, less components and efficient magnetics as far as DC–DC converter is concerned. [ABSTRACT FROM AUTHOR]

The coordinated operation of interconnected but locally controlled electricity markets is generally referred to as a “coupling”. In this paper we propose a new decentralized market mechanism for efficient coupling of independent electricity markets. The mechanism operates after each individual market has settled (e.g. hour-ahead). Based upon the reported supply and demand functions for internal market optimization (clearing), each market operator is asked to iteratively quote the terms of energy trade (on behalf of the agents participating in its market) across the transmission lines connecting to other markets. We show the mechanism’s outcome converges to the optimal flows between markets given the reported supply and demand functions from each individual market clearing. In light of incentive compatibility issues that result from pricing power flows across interconnection lines with locational marginal prices, the mechanism features incentive transfers (updated at each iteration) that compensate each given market with its marginal contribution, i.e. the cost reduction to all other participating markets. We show that these transfers imply truthful participation in the mechanism is a Nash equilibrium. The proposed decentralized mechanism is implemented on the three-area IEEE Reliability Test System where the simulation results showcase the performance guarantees of the proposed mechanism. [ABSTRACT FROM AUTHOR]

In a competitive environment with bid-based markets, power generation companies desire to develop bidding strategies that maximize their revenue. In this paper we ask: What approaches and methodologies have been used to model the bidding problem for hydro-electric producers? We present the problem's developments over time and, through reviewing different variants of the problem, progressively build to the case in which the agent is a price-maker hydro-electric producer. In each variant of the bidding problem, we examine how the approaches used to solve it may or may not be applicable to other variants. Last, for the price-maker hydro-electric producer's bidding problem, we recognize the most recent developments and illuminate a path for future efforts. [ABSTRACT FROM PUBLISHER]

This paper presents a computer-assisted method to teach and understand transmission pricing techniques. The education method is facilitated by specially designed user-friendly transmission pricing software (TPS). The proposed approach helps students fully understand transmission pricing through the fulfillment of the following educational objectives: 1) use of TPS to solve the transmission pricing problem of a small power system as well as a real-world power system; 2) complete understanding of the mathematics involved and development of software code to reproduce part of the results of TPS; 3) analysis of the results, identification and justification of the differences among three transmission tracing techniques and eight transmission cost allocation methods; and 4) extension of TPS methodologies. These methods have been proven effective in the education of students studying power system economics at the National Technical University of Athens (NTUA), Greece. A full-fledged, robust, comprehensive study of the educational improvements achieved through the use of TPS is provided. [ABSTRACT FROM PUBLISHER]

This paper proposes a mathematical model for transmission planning in an environment where there is imperfect competition in electricity supply industry. The model is developed based on the concept of the leader-followers game in applied mathematics. The leader of the game is the transmission planner and the followers are the strategic electricity producing firms. The reaction of the strategic electricity producing firms to the transmission planning decision is modeled using the introduced concept of the worst-Cournot-Nash equilibrium. The worst-Cournot-Nash equilibrium can handle the multiple Nash equilibria problem. The whole mathematical formulation is a bilevel mixed-integer linear programming problem. This formulation is achieved using the Karush-Kuhn-Tucker optimality conditions and a binary mapping approach. The application of the proposed approach to the three-node example system and the modified six-node Garver's example system are studied. The transmission planning with perfect competition is formulated and used as the benchmark. The numerical results show that the proposed approach in this paper can efficiently allocate the additional transmission capacity to the transmission grid such that it reduces the market power cost in the electricity supply industry. However, further studies should be carried out to scale up the proposed approach to the larger case studies. [ABSTRACT FROM PUBLISHER]

Load and price forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and price forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter. [ABSTRACT FROM AUTHOR]

Residual demand curves (RDCs) can be used to represent the strategic interaction of participants in electricity markets. RDCs relate the energy that an agent can buy or sell in one hour with the clearing market price that would be obtained in such hour, assuming the market is organized as simple bid independent auctions. Despite the fact that they have been widely used in the literature, the existence of time and/or spatial constraints in the market clearing algorithm makes the RDCs not directly applicable. This paper tries to overcome these difficulties by extending the concept of RDCs to zonal pricing markets where complex offering conditions and transmission constraints are taken into account. Therefore, the RDCs are redefined in order to capture such effects, which are usually neglected or oversimplified. A new method for computing the redefined RDCs is established and its application to the Iberian electricity market is presented. The results show that modeling complex conditions and transmission constraints in RDCs can have a significant effect when compared to the standard approach found in the literature. Therefore, the method presented in this paper modeling the effect of firm's decisions on market prices in a more accurate way. [ABSTRACT FROM AUTHOR]