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Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves in-sample relative errors of 22.3% and 15.9% for the PTDFs and RAMs respectively, while the out-of-sample ones are 25.4% and 18.1% respectively. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure. [ABSTRACT FROM AUTHOR]

Power systems are undergoing fundamental changes with increasing penetration of renewable energies. A reactive power ancillary service market is of significance to incentivize private providers, such as thermal power units and renewable power plants (RPPs), to participate in maintaining voltage security. Because of the local nature of reactive power, one of the recognized issues is that monopolistic participants may have great market power and strategically manipulate their costs. To pursue economic efficiency, this paper proposes an incentive-compatible mechanism based on Vickery-Clarke-Groves (VCG) auctions for the day-ahead reactive power ancillary service market. The obligations of reactive power ancillary service are quantified in the VCG-based market mechanism. Depending on the expected performance of participants compared with the corresponding obligations, payments are obtained for thermal power units, RPPs, and loads. Hence, providers have the incentive to declare their true costs and undertake obligations. Furthermore, a scarcity price is employed to reward the provision of scarce reactive power resources and ensure reliable market clearing. The proposed mechanism is illustrated using the IEEE 24-bus and 118-bus systems. The results verify the effectiveness of the proposed mechanism. Moreover, high rewards or penalties are observed for voltage weak areas, thereby inducing appropriate investments in reactive power capacities. [ABSTRACT FROM AUTHOR]

In this letter, the evolutionary game theory (EGT) with replication dynamic equations (RDEs) is adopted to explicitly determine the factors affecting energy providers’ (EPs) willingness of using the market power to uplift the price in the bidding procedure, which could be simulated using the win-or-learn-fast policy hill climbing (WoLF-PHC) algorithm as a multi-agent reinforcement learning (MARL) method. Firstly, empirical and numerical connections between WoLF-PHC and RDEs is proved. Then, by formulating RDEs of the bidding procedure, three factors affecting the bidding strategy preference are revealed, including the load demand, severity of congestion, and the price cap. Finally, the impact of these factors on the converged bidding price is demonstrated in case studies, by simulating the bidding procedure driven by WoLF-PHC. [ABSTRACT FROM AUTHOR]

One of the key steps for optimal bidding in power markets is to estimate the rivals’ bidding behaviors. However, for most participants, it would be difficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of individual bids, denoted as aggregated supply curve (ASC), might be helpful to offset the uncertainties of individual bidding behaviors and can be used as reference for optimal bidding. In fact, the real ASC data contains bidding information from thousands of participants, which would be formulated with high dimensionality and unstructured formats, not applicable for general forecasting methods. Thus, a novel data-driven ASC forecasting framework based on long-short term memory (LSTM) model and corresponding data processing techniques is proposed in this paper. In detail, A paradigmatic data integration method is proposed to fix the unstructured data formats. A feature extraction method is developed to simplify the high dimensionality of ASC. Then, a LSTM model is customized to forecast ASCs. At last, real data from Midcontinent Independent System Operator market in the U.S. are utilized to demonstrate the forecasting performance of the proposed framework. [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]

This paper studies the optimal clearing problem for prosumers in peer-to-peer (P2P) energy markets. It is proved that if no trade weights are enforced and the communication structure between successfully traded peers is connected, then the optimal clearing price and total traded powers in P2P market are the same with that in the pool-based market. However, if such communication structure is unconnected, then the P2P market is clustered into smaller P2P markets. If the trade weights are imposed, then the derived P2P market solutions can be significantly changed. Next, a novel decentralized optimization approach is proposed to derive a trading mechanism for P2P markets, based on the alternating direction method of multipliers (ADMM) which naturally fits into the bidirectional trading in P2P energy systems and converges reasonably fast. Analytical formulas of variable updates reveal insightful relations for each pair of prosumers on their individually traded prices and powers with their total traded powers. Further, based on those formulas, decentralized learning schemes for tuning parameters of prosumers cost functions are proposed to attain successful trading with total traded power amount as desired. Case studies on a synthetic system and the IEEE European Low Voltage Test Feeder are then carried out to verify the proposed approaches. [ABSTRACT FROM AUTHOR]

Priority service pricing is a promising approach for mobilizing residential demand response, by offering electricity as a service with various levels of reliability. Higher levels of reliability correspond to higher prices. Proper pricing guarantees that consumers self-select a level of reliability that corresponds to the reliability that the system can offer. However, the traditional theory for menu design is based on numerous stringent assumptions, which may not be respected in practice, such as well-behaved convex cost functions for generators. In this article, we design a priority service menu as the equilibrium solution to a Stackelberg game, which is modeled as a bi-level optimization problem involving a vertically integrated utility and consumers. We reformulate the equilibrium as a mixed-integer problem. As a consequence of this approach, we can integrate the menu design problem within a day-ahead unit commitment model. This allows us to design a menu which exactly meets the profit requirements of a firm. The approach is illustrated on a toy numerical example as well as a large-scale model of the Belgian power market. [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]

Recent advancement of distributed renewable generation has motivated microgrids to trade energy directly with one another, as well as with the utility, in order to minimize their operational costs. Energy trading among microgrids, however, confronts challenges such as reaching a fair trading price, maximizing participants’ profit, and satisfying power network constraints. In this paper, we formulate the direct energy trading among multiple microgrids as a generalized Nash bargaining (GNB) problem that involves the distribution network's operational constraints (e.g., power balance equations and voltage limits). We prove that solving the GNB problem maximizes the social welfare and also fairly distributes the revenue among the microgrids based on their market power. To address the nonconvexity of the GNB problem, we propose a two-phase approach. The first phase involves solving the optimal power flow problem in a distributed fashion using the alternative direction method of multipliers to determine the amount of energy trading. The second phase determines the market clearing price and mutual payments of the microgrids. Simulation results on an IEEE 33-bus system with four microgrids show that the proposed framework substantially reduces total network cost by 37.2%. Our results suggest direct trading need be enforced by regulators to maximize the social welfare. [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]

The increasing penetration of variable energy resources in modern electric power systems requires additional flexibility in reserve provision to maintain reliable and efficient grid operations. However, full recognition and appropriate compensation of this flexibility is difficult to ensure within current power market designs due to rigidity in reserve definitions and requirements. This paper proposes a new mixed-integer linear programming formulation for the optimal clearing of a day-ahead market based on swing contracts with dynamically updated regulation reserve zones. Five-bus and 30-bus test cases are used to illustrate the effectiveness of the proposed new market design. [ABSTRACT FROM AUTHOR]

Increasing use of natural gas for electricity production places added strains on pipeline systems that are used for transporting fuel. Pipeline constraints require power system operators to account for natural gas-supply restrictions in their operational processes. This paper proposes separate optimization models for clearing day-ahead wholesale markets for scheduling power and natural gas systems. We then develop a market-based mechanism that allows for efficient co-ordination of the two systems. Importantly, the co-ordination mechanism only requires the exchange of fuel-price, -supply, and -demand information between the two markets. This can be contrasted with other co-ordination mechanisms that require operations of the two systems by a single entity. Thus, we provide a computationally tractable co-ordination mechanism that does not require the exchange of proprietary information between natural gas and electricity system operators. We demonstrate the effectiveness and scalability of the technique using a numerical example. [ABSTRACT FROM AUTHOR]

Previous work on the role of the demand side in imperfect electricity markets has demonstrated that its self-price elasticity reduces electricity producers’ ability to exercise market power. However, the concept of self-price elasticity cannot accurately capture consumers’ flexibility, as the latter mainly involves shifting of loads’ operation in time. This paper provides for the first time theoretical and quantitative analysis of the beneficial impact of demand shifting (DS) in mitigating market power by the generation side. Quantitative analysis is supported by a multiperiod equilibrium programming model of the imperfect electricity market, accounting for the time-coupling operational constraints of DS as well as network constraints. The decision making process of each strategic producer is modeled through a bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. The oligopolistic market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market reflecting the general generation and demand characteristics of the GB system quantitatively demonstrate the benefits of DS in mitigating producers’ market power, by employing relevant indexes from the literature. [ABSTRACT FROM AUTHOR]

In this paper, an agent-based model is proposed to improve the electricity market efficiency by using different demand response programs (DRPs). In the proposed model, the strategic self-scheduling of each market player in the electricity market and consequent market interactions are considered by using a game theoretic model powered by a security constrained unit commitment. The tariffs of price-based DRPs and the amount of incentive in the incentive-based DRPs are optimized. Furthermore, a market power index and the operation cost are used to evaluate the market efficiency by using a multi-objective decision-making approach. The results show that different types of DRPs differently affect the oligopolistic behavior of market players, and the potential of market power in power systems can be mitigated by employing the proposed model for DRP optimization. Numerical studies reveal that applying combinational DRPs is more efficient when the regulatory body considers both economic and market power targets. [ABSTRACT FROM AUTHOR]

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]

Previous studies of market power within a regional system have considered multiple competing generation operators and the role of transmission constraints. However, these studies typically assume simplified structures in which each operator is restricted to a unique node in a transmission constrained network. Real systems typically have operators making decisions for units in several zones at once. Past studies also implicitly treated market power as a static concept for a given set of market rules and network configuration. We demonstrate that market power is dynamic, and can vary significantly with fuel prices and even with large-scale weather patterns. We also demonstrate the impact on region-wide market power of operators that manage units in multiple zones. We use the Electric Reliability Council of Texas (ERCOT) as an illustrative case study, and apply a conjectured supply function equilibrium (CSFE) approach that accounts for transmission constraints. We show that the companies with greater influence on the market price will depend on the relative prices of coal and natural gas. We also show that a weather event, such as a period without any wind, can have a substantial impact on market power. [ABSTRACT FROM AUTHOR]

Market power assessment is a prime concern when designing a deregulated electricity market. In this paper, we propose a new functional market power measure, termed transmission constrained network flow (TCNF), that unifies three large classes of transmission constrained structural market power indices in the literature: residual supply based, network flow based, and minimal generation based. Furthermore, it is suitable for demand-response and renewable integration and hence more amenable to identifying market power in the future smart grid. The measure is defined abstractly, and allows incorporation of power flow equations in multiple ways; we investigate the current market operations using a DC approximation and further explore the possibility of including detailed AC power flow models through semidefinite relaxation, and interior-point algorithms from Matpower. Finally, we provide extensive simulations on IEEE benchmark systems and highlight the complex interaction of engineering constraints with market power assessment. [ABSTRACT FROM AUTHOR]

With an increasing penetration of wind power, there is likely to be an increasing need for fast-ramping generating units. These generators ensure that no load is lost if supply drops due to the uncertainties in wind power generation. However, it is observed in practice that, in a presence of network constraints, fast-ramping generating units are prone to act strategically and exercise market power by withholding their ramp rates. In this paper we model this gaming behavior on ramp rates. We assume a market operator who collects bids in form of marginal costs, quantities, and ramp rates. He runs a ramp-constrained economic dispatch given the generators' bids, forecasted demand, and contingencies. Following the game-theoretic concepts, we set up a multi-level optimization problem. The lower-level problem is the ramp-constrained economic dispatch and the higher-level represents the profit maximization problems solved by strategic generators. The whole problem is formulated as an equilibrium problem with equilibrium constraints (EPEC). The outcome of the EPEC problem is a set of Nash equilibria. To tackle the multiple Nash equilibria problem, the concept of the extremal-Nash equilibria is defined and formulated. We model the concept of extremal-Nash equilibria as a single-stage mixed-integer linear programming problem (MILP) and demonstrate the application of this mathematical framework on an illustrative case and on a more realistic case study with tractable results. [ABSTRACT FROM PUBLISHER]

Should energy storage buy and sell power at wholesale prices like utilities and generators, or should its physical and financial operation be asynchronyous as with transmission lines? In the first case, storage straightforwardly profits through intertemporal arbitrage, also known as load shifting and peak shaving. In this paper, the latter case, referred to as passive storage, is examined. Because passive storage does not make nodal price transactions, new mechanisms are necessary for its integration into electricity markets. This issue is addressed by further developing the analogy between energy storage and transmission. Specifically, financial rights are defined for storage, and tracing is extended to multiperiod power flows linked by storage. Like flowgate transmission rights, the new financial storage rights redistribute the system operator's merchandising surplus and enable risk-averse market participants to hedge against nodal price volatility resulting from storage congestion. Simple examples are given demonstrating the implementation of the new mechanisms. Game-theoretic analysis suggests that financial storage rights mitigate gaming when both the generator and load bid strategically. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of identifying the optimal investment of a strategic wind power investor that participates in both the day-ahead (DA) and the balancing markets. This investor owns a number of wind power units that jointly with the newly built ones allow it to have a dominant position and to exercise market power in the DA market, behaving as a deviator in the balancing market in which the investor buys/sells its production deviations. The model is formulated as a stochastic complementarity model that can be recast as a mixed-integer linear programming (MILP) model. A static approach is proposed focusing on a future target year, whose uncertainties pertaining to demands, wind power productions, and balancing market prices are precisely described. The proposed model is illustrated using a simple example and two case studies. [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]

As a result of subsidies and technological maturity, renewable electricity producers have grown in some jurisdictions to clearly dominant positions in the market. Under this context, we propose an offering strategy for a wind power producer with market power that participates in the day-ahead market as a price-maker, and in the balancing market as a deviator. Uncertainty pertaining to wind power production and balancing market price is represented through a set of correlated scenarios. The proposed model is a stochastic mathematical program with equilibrium constraints (MPEC) that can be recast as a tractable mixed-integer linear programming (MILP) problem, which is solvable using available optimization software. Results from an illustrative example and two case studies show the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

There have been many debates over whether the energy-only electricity market will be able to provide a reasonable investment signal. So far, many diverse designs of capacity mechanisms have been proposed in relation to the adequacy problem. Researchers have conducted many qualitative studies of capacity mechanisms, but quantitative studies have largely been ignored. The framework for examining the impacts of market designs on generation adequacy considering various generation technologies have not been maturely developed. This paper focuses on developing this framework and finding out how effective capacity mechanisms are in different market conditions. To cover investment under uncertainty, we will introduce different utility functions representing risk attitudes and apply the expected utility theory to the investment decision. Finally, we will examine the investment decision sensitivity of the risk components such as the risk-attitude, the error of forecast peak load, and the forced outage rate and evaluate the performance of three market designs with dynamic simulations: an energy-only market, a capacity payment model, and a capacity demand curve model. [ABSTRACT FROM PUBLISHER]

In this paper, two different approaches to analyzing firm-based market power considering transmission constraints are proposed. One is an application of the transmission-constrained residual demand Jacobian, while the other is a generalization of the “residual supply index” to the case of transmission constraints. These two approaches provide complementary evaluations of market power. Medium- and large-scale system examples are provided to demonstrate computational efficiency, and both approaches could be applied to real-world electricity markets. [ABSTRACT FROM PUBLISHER]

Measuring the exercise of market power has always been a fundamental problem in electricity markets. According to a well-known economic definition, a seller's market power is defined as “the ability to maintain prices profitably above competitive levels for a significant period of time”. This standard definition implies that market power has a fuzzy nature, which leads to the development of a fuzzy system for estimating the exercised market power. As understood from the definition, both duration and magnitude of market power play key roles in the measurement of the exercised market power. The fuzzy estimator gathers these two relevant aspects in a unified framework and uses them to assign a unique measure to the exercised market power. The fuzzy estimator is applied to measure each power supplier's market power in an agent-based computational framework of the considered electricity market. In this agent-based simulation, the power suppliers use Q-learning to learn how to respond to competitors' behavior and market conditions. The experimental results confirm the capability of the proposed fuzzy estimator to monitor and diagnose the exercise of market power. [ABSTRACT FROM PUBLISHER]

The problem of accurately forecasting wind energy has garnered a great deal of attention in recent years. There are always some errors associated with any forecasting methodology. Although it is sometimes assumed that the forecast errors are Normally distributed, it is a special case arising from the geographical dispersion of wind resources, as shown in this paper. The distribution of the forecast error needs to be examined individually for every wind farm to determine the impact of this error on trading energy in electricity markets. This paper addresses the problem of modeling the distribution of the forecast errors associated with Persistence forecasts at the level of a single wind farm, and develops a novel, mixed distribution-based model to approximate the distribution of these errors. The model is then used to estimate the penalties for imperfectly forecast energy injections in the short-term markets. The results from the application of this model to trading are further used to assess the feasibility of energy storage in hedging against imperfect forecasts. [ABSTRACT FROM PUBLISHER]

This paper proposes an active-reactive power dispatch procedure that is expressed via a bilevel optimization problem whose upper and lower level criteria are, respectively, the minimum opportunity cost and minimum offered price of active power. It analyzes the impact of minimizing such criteria on system operation, and it investigates the behavior of marginal prices and opportunity costs and their use in mechanisms to compensate generators for power provision. The bilevel problem is solved using a version of interior point methods derived for mathematical programs with complementarity constraints. Results obtained with three test systems are analyzed. [ABSTRACT FROM PUBLISHER]

An accurate estimation of critical load levels (CLLs) is beneficial in evaluating the confidence level for the predicted locational marginal prices (LMPs) in power markets. This paper proposes an efficient algorithm to quickly identify CLLs under the with-loss-DCOPF framework. The proposed method approximates the trajectory of DCOPF solutions with respect to the system load variations. The quadratic coefficients of the trajectory can be efficiently obtained by solving a small-scale nonlinear system of equations formed by the variable substitution method, which does not require additional OPF runs and is thus more computationally efficient than existing methods such as binary search and interpolation. The obtained trajectory can be used for a quick calculation of OPF solutions without re-running OPF. Further, through trajectory extrapolation, new congestions and other binding constraints can be identified simultaneously with the CLL predictions. Particularly, the proposed method is not restricted to a specific DCOPF-based model and can be easily applied to other DCOPF-based variants. Numeric studies on a revised IEEE 30-bus system are presented to illustrate the high computational efficiency and accuracy of the approach. It is further tested on the IEEE 118-bus system using four different DCOPF-based models to illustrate its model adaptability. [ABSTRACT FROM PUBLISHER]

This paper describes a numerical approach to solving the mathematical structure proposed in the first part of this paper. The numerical approach employs a standard genetic algorithm (GA) embedded with an island parallel genetic algorithm (IPGA). The GA handles the decision variables of the transmission network service provider, (TNSP) while the IPGA module finds the equilibrium of the electricity market. The IPGA module uses the concept of parallel islands with limited communication. The islands evolve in parallel and communicate with each other at a specific rate and frequency. The communication pattern helps the IPGA module to spread the best-found genes across all isolated islands. The isolated evolution removes the fitness pressure of the already-found optima from the chromosomes in other islands. A stability operator has been developed which detects stabilized islands and through a strong mutation process re-employs them in exploring the search space. To improve the efficiency of the proposed numerical solution, two high performance computing (HPC) techniques are used—shared-memory architecture and distributed-memory architecture. The application of the proposed approach to the assessment of transmission augmentation is illustrated using an IEEE 14-bus example system. [ABSTRACT FROM PUBLISHER]

This paper proposes a bilevel model to assist a generation company in making its long-term generation capacity investment decisions considering uncertainty regarding the investments of the other generation companies. The bilevel formulation allows for the uncoupling of investment and generation decisions, as investment decisions of the single investing generation company are taken in the upper level with the objective to maximize expected profits and generation decisions by all companies are considered in the lower level. The lower level represents the oligopolistic market equilibrium via a conjectured-price response formulation, which can capture various degrees of strategic market behavior like perfect competition, the Cournot oligopoly, and intermediate cases. [ABSTRACT FROM PUBLISHER]

This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive model, based on the Generalized Rule Induction (GRI) model. This predictive analysis discovers association rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company. [ABSTRACT FROM PUBLISHER]

The integration of the spot electricity markets in Europe shall lead to multi-area power exchanges that will substitute the local markets. In such scheme, market prices are affected by physical (e.g., network) constraints, yet they should sometimes follow regulatory policy rules, which do not necessarily reflect or depend on physical characteristics. In some cases, complex pricing rules should be implemented, which impose price discrimination for supply and demand entities within the same area. The methodology presented in this paper enables the balancing of supply and demand in a multi-area market considering energy and reserve bids, under complex pricing rules, which mix energy and reserve prices. A demand bid corresponds to the whole cost a demand entity is willing to pay for its participation in the energy market, including the cost for the procurement of the necessary reserves. The approach attains price integration of energy and reserves markets, simultaneous settlement of energy and reserves, and significant decrease of the payments through the uplift accounts. The main principle is the formulation of a mixed complementarity problem for the system equilibrium conditions, in which supply and demand are associated to explicitly or implicitly defined prices, which may be different even in the same zone. [ABSTRACT FROM PUBLISHER]

Short-term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning. This study proposes a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns—combinations of status flags for generating units and transmission lines. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

A yearly stochastic self-scheduling model for a price-maker hydro producer is presented. In the short term, the producer aims at maximizing profits in the day-ahead market. Residual demand curves model the producer's interaction with his competitors and the load demand. A modification of the residual demand curve is proposed to enable the definition of optimal pumped-hydro bids. Multistage stochastic programming provides hedging against the mid-term uncertainty of inflows, load demand, and competitors' offers. The proposed method models the impact of short-term profit maximization decisions on mid-term scheduling within a compact stochastic mixed integer linear programming (MILP) approach. A test case with a three-stage, 90-scenario stochastic tree and data of the Greek Power System is presented. Results provide insight on both mid-term reservoir management and short-term market-based operation and indicate the feasibility of solving a very large-scale scheduling problem as a unique MILP using a commercial solver. The role of forward contracts in mitigating market power is also analyzed. [ABSTRACT FROM PUBLISHER]

Market power gives certain market participants the ability to manipulate the market to their advantage when their product is not substitutable by competitors. Identification of generators which have the potential for market power either individually or within a small group is performed using sensitivity information from the linear programming optimal power flow (LP OPF). The impact of network constraints on admissible price perturbations are used to group generators that have the potential to exhibit local market power. Specific price perturbation vectors are found that highlight a constraint-induced locational advantage for these suppliers. In practice, this is most commonly observed in “load pockets,” for which ISO policies mitigate market power. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce four new indices of market power in transmission-constrained electricity markets that are based on economic principles together with faithful representation of the effects of Kirchhoff's laws. [ABSTRACT FROM PUBLISHER]

This paper seeks to characterize the nonlinear effects of exogenous factors on wholesale electricity price formation. Using a special type of regime-switching, the logistic smooth transition regression model, we capture the mean-reverting and spiky characteristics of spot prices, and at the same time, estimate their rather complex relationships to fundamentals. We show that for different trading periods within the day, prices can be a function of fundamentally different drivers. In addition to including the usual fuel price, demand and reserve margin drivers, this analysis tests whether a market concentration variable is as significant in the high-frequency context as it is known to be over the longer term. The analysis is also innovative in testing the nonlinear influence of carbon emission trading. [ABSTRACT FROM AUTHOR]

The California energy crisis in 2000-2001 showed what could happen to an electricity market if it did not go through a comprehensive and rigorous testing before its implementation. Due to the complexity of the market structure, strategic interaction between the participants, and the underlying physics, it is difficult to fully evaluate the implications of potential changes to market rules. This paper presents a flexible and integrative method to assess market designs through agent-based modeling. Realistic simulation scenarios are constructed for evaluation of the proposed PJM-like market power mitigation rules of the California electricity market. Simulation results show that in the absence of market power mitigation, generation company (GENCO) agents facilitated by Q-learning are able to exploit the market flaws and make significantly higher profits relative to the competitive benchmark. The incorporation of PJM-like local market power mitigation rules is shown to be effective in suppressing the exercise of market power. [ABSTRACT FROM AUTHOR]

This paper proposes two mathematical structures for considering the market power effect of transmission capacity in transmission augmentation assessment. These mathematical structures use the concepts of monopoly rent and quantity withheld in economics for market power modeling in the assessment process of transmission augmentation. The simultaneous-move and sequential-move games in applied mathematics are used to model the interactions of the transmission network service provider, generating companies, and the market management company in the proposed mathematical structures. The solution concept of Nash equilibria is reformulated as an optimization problem, and the multiple Nash equilibria is tackled through an introduced concept termed worst Nash equilibrium. A numerical solution is developed to solve the proposed mathematical structures. The numerical solution is an island parallel genetic algorithm nested in a standard genetic algorithm. The six-bus Garver's example system and the IEEE 14-bus test system are modified and studied. The results prove the strong mechanism of the developed structures for modeling the market power effect of transmission capacity in the assessment of transmission augmentation. [ABSTRACT FROM AUTHOR]

To study the competitive behavior among individual generating companies (GENCOs), an incomplete information game model is proposed in this paper in which each GENCO is modeled as an agent. Each agent makes strategic generation capacity expansion decisions based on its incomplete information on other GENCOs. The formation of this game model falls into a bi-level optimization problem. The upper level of this problem is the GENCOs' own decision on optimal planning strategies and energy/reserve bidding strategies. The lower-level problem is the ISO's market clearing problem that minimizes the cost to supply the load, which yields price signals for GENCOs to calculate their own payoffs. A co-evolutionary algorithm combined with pattern search is proposed to optimize the search for the Nash equilibrium of the competition game with incomplete information. The Nash equilibrium is obtained if all GENCOs reach their maximum expected payoff assuming the planning strategies of other GENCOs' remain unchanged. The physical withholding of capacity is considered in the energy market and the Herfindahl-Hirschman Index is utilized to measure the market concentration. The competitive behaviors are analyzed in three policy scenarios based on different market rules for reserve procurement and compensation. [ABSTRACT FROM AUTHOR]

This paper provides new results that cast doubt on the conventional assumption that introducing voluntary forward markets will mitigate the market power of electricity generating companies by encouraging them to contract forward. We provide complementary insights by developing an agent-based simulation model of an actual system. This facilitates understanding the strategy selection of heterogeneous companies through computational learning. We use a detailed model of the Spanish system, where companies trade through a uniform price, pool-based spot market. We model market power in this pool through agents offering to generate with conjectured price responses estimated from supply function equilibrium assumptions. We envisage the introduction of a forward market, with price formation following the conventional financial perspective of expected spot plus a risk premium. We find, in general, that larger companies prefer to exercise market power in the spot market, while smaller companies prefer to contract forward, but strategy selection itself can be quite a delicate, situation specific process. [ABSTRACT FROM AUTHOR]