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As climate concerns, low natural gas prices, and renewable technologies increase the electric power sector's dependence on natural gas-fired power plants, operational and investment models for gas and electric power systems will need to incorporate the interdependencies between these two systems to accurately capture the impacts of one on the other. Currently, few hybrid gas–electricity models exist. This paper reviews the state of the art for hybrid gas–electricity models and presents a new model and case study to illustrate a few potential coupling effects between gas and electric power systems. Specifically, the proposed model analyzes the optimal operation of gas-fired power plants in a competitive electricity market taking into consideration gas purchases, gas capacity contracting, and residual demand uncertainty for the generation company due to renewable energy sources. [ABSTRACT FROM PUBLISHER]

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 is concerned with the problem of Security-Constrained Unit Commitment (SCUC) which is a long-standing challenge in power system engineering faced by system operators and utility companies on a daily basis. We consider a detailed variant of this problem that suffers from complexities posed by the presence of binary variables, the uncertainty of renewable sources and security constraints. A convex relaxation is formulated which is capable of finding feasible solutions within a provable distance from global optimality. We demonstrate the performance of this approach on detailed and challenging instances of SCUC with IEEE and PEGASE benchmark cases from Matpower. The proposed approach is able to handle over 12,000 binary variables and 2 million continuous variables with significant improvement in solution quality over commonly-used off-the-shelf solvers and other methods of convex relaxation. [ABSTRACT FROM AUTHOR]

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

With the increasing use of renewable energy resources in power systems, it is necessary to overcome the limitations of these resources in terms of the supply and demand balance in high-voltage power systems. A viable solution is employing zero net energy community microgrids, which can manage the demand locally and minimize the impact to the power grid. To this end, this paper proposes an optimal scheduling method for a zero net energy community microgrid with customer-owned energy storage systems (CES). It is assumed that the microgrid operator operates in the CES market based on a bilateral contract. The CES aggregators constitute the CES and participate in the market. The aggregated CES (ACES) is considered as a single energy storage system in the proposed scheduling method. Numerical examples are presented to demonstrate the effectiveness of the proposed method.  [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]

Network use-of-system (UoS) tariffs play an essential role in the deregulated power markets to recover network investment and maintenance costs from network users, and send economic signals to influence the users' behaviors in using systems. The tariffs are essential to the business operation of both network operators and users. They, however, could have great volatility due to the uncertainties from both external and internal factors. Such uncertain tariffs could bring severe adverse impacts to network users, which justifies that there is an urgent need to reduce the tariff volatility and improve its predictability. This paper for the first time investigates the variations in network tariffs that cause great risks to network users and then designs financial tools to reduce the volatility. The causing factors of the variations are categorized into global and local groups according to their different features. After introducing the process from network planning, charging to revenue reconciliation, the paper discusses the benefits of hedging for network users. In order to reduce tariff volatility, it proposes a novel risk management strategy to maintain the consistency of tariffs. It is achieved by designing long-term contracts using financial hedging. The value of hedge contracts is decided by three key factors: hedged load/generation percentage, hedged price, and risk premium. The hedged part is charged at hedged prices and the non-hedged part is charged at actual prices, on top of which customers needs to pay extra for the risk premium. The paper also designs an optimal decision-making tool to assist network users to manage long-term contracts in order to reduce total tariff costs. As demonstrated in the case study, the proposed long-term products can effectively reduce tariff volatility for network users and create a safe environment for their business operation. [ABSTRACT FROM PUBLISHER]

This paper develops a decision making framework for mid-term scheduling problems of the large industrial consumers. The proposed approach is based upon the recently introduced class of stochastic programming problems, established by the concept of second-order stochastic dominance (SSD). In this paper, it is assumed that the electricity price and the rate of availability (unavailability) of the generating unit (forced outage rate) are the sources of uncertainty in the decision-making problem. In the developed SSD-constrained stochastic programming problem, the consumer risk is managed by minimization of the pool procurement, as the objective function, and economic issues (e.g., cost minimization) are considered in the SD constraint. Furthermore, while most approaches optimize the cost subject to an assumed demand profile, our method enforces the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e., three months (12 weeks), so that the total production will remain constant. Simulation results for a typical cement factory (as an industrial consumer) and comparison to a mean-risk approach with CVaR risk measure, revealed the interesting results and benefits of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper proposes an incentive-based coordination mechanism between a wind energy supplier (WES) and a conventional energy supplier (CES) to achieve a Pareto improvement. To comply with its day-ahead schedules and hedge against the intermittent wind energy generation, the WES is allowed to outsource a back up power capacity from the CES via making a bilateral contract. However, unanimous agreement cannot always be achieved since each party plays on its own interest. We employ the concept of swing option contracts to further encourage the suppliers to reach an agreement of the contract. On the one hand, the WES can leverage the uncertainty of wind output by covering possible energy shortage from the CES. On the other hand, the CES can optimally allocate its energy capacity by participating into the electricity market and offering capacity to backup the shortage of energy from the WES. The bidding problem for each supplier is formulated as a multi-stage stochastic programming model, with the objective of maximizing the expected profit while maintaining a low level of risk. Unlike the traditional two-stage approach, the proposed multi-stage model can effectively capture the impact of rebidding process in the real-time market. We incorporate conditional-value-at-risk as a risk measure to characterize the effect of risk perception of suppliers on their bidding decisions. Meanwhile, a game theory based approach is developed to obtain the contract items between the suppliers. Implementation results on real cases are provided to illustrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]

Long-term contracting is the traditional governance mechanism to deal with the transaction costs associated with the specificity of gas industry assets. Long-term contracts have been often used to allocate risks among players, and to that end, they often include take-or-pay provisions. These clauses specify that buyers take the volume risk, as they are obliged to pay for a minimum amount of gas consumption. In exchange, buyers pay a predefined price, supposedly lower than the risk-neutral expectation of short-term gas prices. In that view, if the buyer is able to resell that gas in the short term, the contract is an effective hedge against short-term volatility. Otherwise, the contract does not act as a hedge but it becomes a sunk cost. The corresponding power producers' behavior involves not only output decisions but also financial decisions. To analyze that situation, this paper develops a new quantitative methodology that allows comparing risk-neutral valuations of gas and power markets decisions. We test the model in a real-size system, and show the additional cost of the power system associated with a possible illiquidity of the short-term gas market. [ABSTRACT FROM PUBLISHER]

Portfolios of renewable electricity sources are interesting risk-management mechanisms for trading in electricity contract markets. When formed by players belonging to different companies, their stability relies on the way the benefit generated by the optimal portfolio is allocated. The challenge of finding a fair and efficient allocation can be mathematically formulated in terms of finding the Core of a cooperative game, which in turn is stated as an optimization problem with a set of constraints that exponentially grows with the number of participants, quickly becoming computationally intractable. Moreover, the right-hand-side of each constraint relies on a given coalition value, which in our case is obtained by a two-stage stochastic optimization model. This paper presents an efficient methodology based on mixed —linear programming and Benders decomposition to find the Nucleolus share of large-scale renewable portfolios. Case studies are presented with data from the Brazilian power system. [ABSTRACT FROM PUBLISHER]

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 deals with wind power offering strategies in day-ahead markets. A new plan is proposed in which a wind power producer participates in the day-ahead market while employing demand response (DR) to smooth its power variations. In this context, a new DR scheme is presented through which the wind power producer is able to achieve DR by establishing various DR agreements with DR aggregators. The proposed offering plan involves two stages: the first stage clears on the day-ahead market. The wind power producer decides on day-ahead offers as well as DR agreements with the aggregator. The second stage takes place on the balancing market. In a successive approach, the wind power producer determines its energy trading for each period until the whole day is covered. Additionally, proper DR agreements for each period are confirmed here. The proposed plan is formulated in a stochastic programming approach, where its validity is assessed on a case of the Nordic market. [ABSTRACT FROM PUBLISHER]

The penetration of distributed renewable energy sources in the distribution grid is increasing considerably in the last years. This is one of the main causes that contributed to the growth of technical problems in both transmission and distribution systems. An effective solution to improve system security is to exploit the flexibility that can be provided by distributed energy resources (DER), which are mostly located at the distribution grids. Their location combined with the lack of power flow coordination at the system operators interface creates difficulties in taking advantage of these flexible resources. This paper presents a methodology based on the solution of a set of optimization problems that estimate the flexibility ranges at the distribution and transmission system operators (TSO-DSO) boundary nodes. The estimation is performed while considering the grid technical constraints and a maximum cost that the user is willing to pay. The novelty behind this approach comes from the development of flexibility cost maps, which allow the visualization of the impact of DER flexibility on the operating point at the TSO-DSO interface. The results are compared with a sampling method and suggest that a higher accuracy in the TSO-DSO information exchange process can be achieved through this approach. [ABSTRACT FROM AUTHOR]

Many countries use auctions to select agents to which transmission concessions are awarded. Where multiple concessions are auctioned each year, there are potential benefits, for transcos and grid users, in using auction protocols allowing risk-averse bidders to explicitly consider complementary packages of transmission facilities: combinatorial and simultaneous descending auctions. We investigate the use of these protocols for transmission auctions, with aid of mixed-integer linear optimization models developed for this task and realistic case studies, while focusing on the treatment of the exposure problem. [ABSTRACT FROM AUTHOR]

Within a weekly market horizon, this paper considers a power producer that sells its energy both in the pool and through weekly forward contracts. The paper provides a methodology that allows the producer to derive the self-scheduling of its production units, to select weekly forward contracts, and to obtain the offering strategy for Monday's pool. The proposed technique is based on stochastic programming and allows the producer to maximize its expected profit while controlling the risk of profit variability. A comprehensive case study is used to illustrate the characteristics of the proposed methodology. Appropriate conclusions are finally drawn. [ABSTRACT FROM AUTHOR]

Non-generating resources such as thermostatically controlled loads (TCLs) can arbitrage energy prices and provide balancing reserves when aggregated due to their thermal energy storage capacity. An aggregator that wants to quantify this flexibility in order to place optimal bids before gate closure, would be affected by market timing. The market timing can be quantified by parameters such as lead time and contract period. This paper explores the impact of market timing on TCL aggregate power consumption and reserve capacity bids and quantifies trade-offs between market timing and flexibility. We first optimize the power consumption and reserve capacity offers at given lead times and contract periods, varying from 24 hours ahead to real-time. We then introduce uncertainty in prices and TCL availability, formulate a two-stage chance-constrained optimization problem of a risk-averse aggregator, implement it on a rolling horizon basis, and evaluate how the trade-offs change. The results show that shorter lead times and contract periods positively impact TCL profit as well as flexibility if the prediction horizon is sufficiently long. The proposed method can be used by aggregators to decide on optimal bids and incentive payments to consumers to reward flexibility. [ABSTRACT FROM AUTHOR]

Considering the viewpoint of a retailer, this paper analyzes the problem of setting up contracts on both the supplier and end-user sides to maximize profits while maintaining an acceptable level of settlement risk. The proposed stochastic optimization model can assist retailers with these efforts and guide them in their contractual arrangements. A realistic example illustrates the capabilities of the methodology proposed. [ABSTRACT FROM AUTHOR]

Demand response could provide reserves to power systems and market designs should enable cost-efficient exploitation of these resources. In direct load control programs, consumers provide demand response in exchange for incentive payments. The success of such contracts is a function of level of information sharing, consumer costs, and power system constraints/costs. In this paper, we compare three approaches to contract design, each of which assumes different levels of consumer privacy. Our aim is to explore tradeoffs between privacy, resource exploitation, and the reward earned by the load aggregator. The first approach assumes full and truthful information exchange between the consumer and the system operator. We then develop a novel centralized approach in which an aggregator applies mechanism design to offer contracts to consumers who reveal partial cost information. A novel decentralized approach is developed in which consumers act cooperatively to pool demand response reserves, enabling them to keep individual cost information private. Through simulation studies, we find that, in the centralized approach, resource exploitation/rewards are highly sensitive to the accuracy of the load aggregator's consumer cost curves approximations. We also find that in the decentralized approach, rewards can be small, especially if information exchange is costly. [ABSTRACT FROM AUTHOR]

A microgrid can be a useful entity to support stable and efficient operation of power systems with large-scale penetration of distributed generators. Suppose that a microgrid should provide reserve operation in a decentralized environment; determining a suitable reserve capacity becomes an important problem for reliable and economic operation. To address this problem, in this paper we present a method of determining the optimal reserve capacity under the power exchange for frequency control (PXFC) market environment. After creating some penalty rules that take microgrid islanding into account, determining the optimal reserve capacity is formulated as an optimization problem. Microgrid islanding probability (MIP) is proposed as an index to describe the likelihood of microgrid islanding. The effectiveness of the method in terms of the operating cost is investigated using simulations, where our method and two further methods are applied to microgrids with different generation capabilities. The properties of MIP are also demonstrated by the simulations. [ABSTRACT FROM PUBLISHER]

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

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]

This paper introduces a decentralized market design, which allows a distribution system operator to manage local demand constraints by obtaining flexibility from competing aggregators, which must in-turn incentivize prosumers to provide this flexibility. The novel networked market structure accounts for distribution system operator-to-aggregator and aggregator-to-prosumer ICT infrastructure and contractual arrangements, which may limit which participants can negotiate transactions with one another. The proposed flexibility market is opt-in for prosumers, which continue to obtain energy within the existing retail electricity market. At the same time, it is underpinned by bilateral energy transactions, and could be integrated into future peer-to-peer electricity markets. Through the market, the distribution system operator, aggregators, and prosumers reach agreement on a stable outcome–a set of individually beneficial transactions no group wishes to mutually deviate from. Market outcomes also satisfy Pareto efficiency, meaning that it is not possible to make a participant better off, without making another worse off. [ABSTRACT FROM AUTHOR]

This paper presents an original methodology to design a financial product that could enhance the demand side participation in ancillary services, especially for industrial consumers. The financial product consists in an American option on the Spanish secondary reserve market for the following day, where the buyer has the right, but not the obligation to offer part of their capacity to the system operator. Considering this approach, an industrial consumer would receive an economic incentive to offer its flexibility to the system without changing its production planning, paying an upfront premium. The computation of the American option is based on a Monte Carlo simulation approach where the random paths are obtained from a machine learning model. The machine learning model attempts to forecast the 24 hour secondary band prices of the following day using a combination of different algorithms; the output and the error of the model are used as a baseline to perform the Monte Carlo simulation that computes the option value. [ABSTRACT FROM AUTHOR]

Fossil fuel generation companies (GenCos) trade in multiple uncertain energy markets: fuel and carbon markets on upstream side while electricity market on downstream side. Global economic and environmental benefits lead these markets to pursue overlapping goals, making them highly interactive. GenCos may identify optimal trading strategies for upstream and downstream trading in an integrated framework, to manage an overall secure and profitable position. Further, severe unpredictability of energy market prices may necessitate a GenCo to make trading plans which perform better meeting its goals. Under severe uncertainty of involved markets, this paper proposes Information Gap Decision Theory (IGDT) based approach to select three interrelated trading portfolios, in an integrated framework. Results from a realistic case study provide a comprehensive decision insight to address risk-averse and risk-seeking behavior of GenCo, explicitly highlighting importance of co-variation in prices of interactive markets. [ABSTRACT FROM PUBLISHER]

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated. [ABSTRACT FROM PUBLISHER]

Natural gas and electricity systems are becoming increasingly strongly coupled. Gas-fired units (GFUs) are replacing retired coal plants, and the power systems are more dependent on the flexibilities provided by GFUs. The GFUs’ power generation capability relies on the availability of gas resources, which is jointly determined by the capacity of gas suppliers and pipeline networks. However, the gas and electricity markets are operated separately. Consequently, the GFUs are forced to “represent” the entire power system to bid on the gas market: they must make forecasts regarding future gas consumption and bear the risk of improper contracts or being unable to meet generation schedules due to occasional insufficient gas supply. When facing larger shares of renewable energies and more frequent gas network congestions, the current market framework is particularly unreliable and inefficient, as well as economically unfriendly to the investors of the GFU assets. In this paper, we try to develop a framework that can combine the two markets. By properly pricing the scarce resources, e.g., gas transmission capacity, the joint market can help us to allocate the resources more efficiently while satisfying the demands. Moreover, a more forward-looking day-ahead market clearing framework is presented by considering the uncertainty brought by renewable energies. The formulation and algorithm of the proposed joint market model will be presented, as are some case studies. [ABSTRACT FROM PUBLISHER]

The Endesa Company is the main power utility in Spain. One of the main concerns of power distribution companies is energy loss, both technical and non-technical. A non-technical loss (NTL) in power utilities is defined as any consumed energy or service that is not billed by some type of anomaly. The NTL reduction in Endesa is based on the detection and inspection of the customers that have null consumption during a certain period. The problem with this methodology is the low rate of success of these inspections. This paper presents a framework and methodology, developed as two coordinated modules, that improves this type of inspection. The first module is based on a customer filtering based on text mining and a complementary artificial neural network. The second module, developed from a data mining process, contains a Classification & Regression tree and a Self-Organizing Map neural network. With these modules, the success of the inspections is multiplied by 3. The proposed framework was developed as part of a collaboration project with Endesa. [ABSTRACT FROM PUBLISHER]

An important problem in modern electrical power systems is the inherent variability and uncertainty of renewable energy resources. Allocating the costs generated by the variable resources in a just and reasonable manner is crucial for economic efficiency. In this paper, we develop an axiomatic framework for allocating the deviation costs generated by variable resources using the cost causation based principle. The cost causation principle has a long historical tradition in electrical energy systems and has been proved to provide economic efficiency. We apply the new cost causation based framework to allocate the cost in the production deviation of a group of renewable energy producers and thereby help promote the market integration of renewable energy. The resulting cost allocation framework is then applied to five wind power producers bidding in the Iberian electricity market in Europe, using real data on wind speeds and market prices. [ABSTRACT FROM PUBLISHER]

Most current regulations allow small-scale electric generation facilities to participate in distributed generation (DG) with few requirements on power-purchase agreements. However, in this paper, it is shown that distribution companies can alternatively encourage DG investors into DG contracts that can significantly benefit the utility network. In this regard, a new algorithm is proposed to determine the best sites, sizes, and optimal payment incentives under such special contracts for committed-type DG projects to offset distribution network investment costs. On one hand, the aim is to allocate DGs such that the present value profit gained by the distribution company is maximized via procuring power from DGs and the market at a minimum expense. On the other hand, each DG unit's individual profit is taken into account to assure that private DG investment remains economical. The algorithm is verified in various cases and the impacts of different factors are accordingly studied. [ABSTRACT FROM PUBLISHER]

This paper is the second of a two-paper series and presents a model to assess and promote investment projects defined in a plan of expansion of the transmission. We propose a model that consists of three main elements: valuation of a project based on the design of a linear contract, a principal-agent model to assess the optimal effort of an agent, and the right-of-way negotiating cost. We also define a model to evaluate bids by the agents. The value of the project depends on the number of competitors, the incentives to invest, and the right-of-way costs. The right-of-way cost is approached from the perspective of a bilateral bargaining problem. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of the self-scheduling of thermal generating units during commissioning. A 0/1 mixed-integer linear formulation is presented, which allows an accurate and realistic modeling for the scheduling of the commissioning tests that should be performed once the construction of the thermal unit has been completed and prior to entering its commercial operation. A flexible contract between the producer and the contractor regarding the performing period of the commissioning tests is proposed. The model presented can be used by a producer with thermal units in commissioning, who acts either as a price-taker or a price-maker in the day-ahead energy market. Test results on a medium-scale real test system address the effect that the implementation of the proposed model has on the producer profits as well as on the day-ahead market clearing prices. [ABSTRACT FROM PUBLISHER]

In restructured power systems, there are many independent players who benefit from demand response (DR). These include the transmission system operator (TSO), distributors, retailers, and aggregators. This paper proposes a new concept—demand response eXchange (DRX)—in which DR is treated as a public good to be exchanged between DR buyers and sellers. Buyers need DR to improve the reliability of their own electricity-dependent businesses and systems. Sellers have the capacity to significantly modify electricity demand on request. Microeconomic theory is applied to model the DRX in the form of a pool-based market. In this market, a DRX operator (DRXO) collects DR bids and offers from the buyers and sellers, respectively. It then clears the market by maximizing the total market benefit subject to certain constraints including: demand-supply balance, and assurance contracts related to individual buyer contributions for DR. The DRX model is also tested on a small power system, and its efficiency is reported. [ABSTRACT FROM PUBLISHER]

This paper applies the conceptual framework applied in the work of Oum to hedge retailers against price-quantity fluctuations in spot electricity markets, and extends it to power generators, in order to design suitable power options with optimal strike prices from a market maker's perspective. These options are then used to hedge agents against price and quantity fluctuations by maximizing a static expected utility problem. An infinite collection of derivatives (“exotic option”) emerges as the solution of both price and quantity hedging. This exotic option is approximated with a portfolio composed by bonds, forward/futures contracts, and a fixed number of put and call options, employing a plausible replicating strategy. The theoretical framework is tested within the context of the Colombian power market, and is applied to month-ahead and quarterly-ahead hedging during on-peak hours. The proposal addresses major problems such as lack of liquidity and anonymity of the current bilateral electricity trading scheme in Colombia. [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]

One of the most important daily decisions that a Genco has to make is to allocate generation assets between the forward and spot markets. That is, how much capacity should be contracted in the forward market and how much should be kept to bid in spot market? This paper focuses on generation asset allocation between monthly forward contracts, such as bilateral contracts, futures contracts, options contracts, and daily spot markets, considering operating costs and constraints of generating units, as well as spot price risk. The problem is to find the optimal hedging position based on the known forward price and the forecasted hourly spot prices and is formulated based on the model of PJM market. The double dynamic programming method is applied to solve this optimal asset allocation problem with all the short-term operating constraints of the generating unit satisfied. Three types of forward contracts that are commonly used in practice are considered and their impact on generation asset allocation are analyzed and compared. The analytic relationship between the optimal contract quantity of a particular type and spot generation is established. Based on this relationship, the applicability and characteristics of a particular type of contract are discussed. Furthermore, with the solution to the generation asset allocation problem as a basis, the pricing strategy in the forward market is analyzed. A Nash game model is established and an iterative process is developed to determine the equilibrium pricing of futures contracts. Numerical testing shows that the method for the generation asset allocation is effective. Various factors influencing the decision of generation asset allocation and the relationship between futures contract price and spot price are tested and analyzed. [ABSTRACT FROM AUTHOR]

A generating unit can sell its production in the pool facing the financial risk inherent to volatile pool prices. Alternatively, it can sell its production through the futures market at comparatively stable but smaller average prices. Unlike the pool, forward contracts involve an obligation to sell during a specified time period, and, if the unit fails, it should buy energy from the pool to meet its futures market commitments. Hence, taking into account its forced outage rate, the unit should determine the appropriate mix of pool and futures-market involvement so that its expected profit is maximized for a prespecified risk level on profit variability. Within this context, this paper analyzes the impact of the degree of unavailability of the generating unit on its forward contracting decisions. A detailed case study illustrates the analysis performed. [ABSTRACT FROM AUTHOR]

In competitive electric energy markets, the power generation dispatch optimization is one of the most important missions among generation companies—how to respond to the markets, dispatch their units, and maximize profits. This paper proposes an approach to incorporate power contracts, which include call and put options, forward contracts, and reliability must-run contracts, into multi-area unit commitment and economic dispatch solutions. The proposed solution algorithm is based on adaptive Lagrangian relaxation, unit decommitment, and lambda-iteration methods. The problem formulation consists of three stages: 1) the incorporation of the power contracts, 2) the multi-area unit commitment, and 3) the multi-area economic dispatch. The proposed algorithm has been successfully implemented, and its testing results on modified IEEE test cases are promising. [ABSTRACT FROM AUTHOR]

This paper provides a technique based on stochastic programming to optimally solve the electricity procurement problem faced by a large consumer. Supply sources include bilateral contracts, a limited amount of self-production and the pool. Risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study are provided and analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a transmission contract for reliability and risk-hedging in the deregulated power system. The proposed transmission contract, which is conducted by the transmission system operator (TSO), is a product of the agreements between the generation company (GENCO), the TSO, the distribution company (DISCO), and the large consumer. The proposed transmission contract results in a certain income of the GENCO and provides an incentive for new investments to meet demand in the future. Furthermore, it can help the DISCO and the large consumer hedge the risks, which are caused by the volatility of the price in the spot market. Additionally, for the large consumer, whose load needs high reliable electricity, the proposed contract gives a guarantee that his or her load will always be supplied, or otherwise, he or she will receive a compensation for the service interruption. The effectiveness, especially from the consumer's viewpoint, is shown by using the simulation based on two market models: pool model and mixed pool/bilateral model. [ABSTRACT FROM AUTHOR]

During the last 15 years, the regulatory framework for the wholesale sector of the electricity industry has been replaced in many countries by market competition. In such an environment, electric firms compete through both spot market bidding and bilateral contract trading. Firms have to consider their forward contract positions when they make spot market decisions. In this paper, we propose a linear asymmetric supply function equilibrium (SFE) model with transmission constraints to develop firms' optimal bidding strategies considering forward contracts. The characteristics of firms' behaviors are analyzed under the induced equilibrium conditions. The model is applied to the real-time energy market of the Electric Reliability Council of Texas (ERCOT). The effects of forward contracts on the ERCOT market are evaluated. It is shown that the model is able to capture features of bidding behaviors in the ERCOT market. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic time simulation model for long-term generation capacity investment decisions in the presence of either perfect or imperfect electricity market. The model is based on system dynamics concept in which the dynamics of capacity construction is traced using the forecast of electricity price cleared in the short-term electricity market. Both the perfect and oligopolistic competitions are considered and a market power index is defined to evaluate the competition level of the electricity market. The short-term and long-term dynamic analysis are used to represent the generation firms' behavior in bidding strategy and capacity investment, respectively. Both possibilities of using forward bilateral contracts and application of wind power generation have been assessed individually in the proposed model in order to mitigate the market power. The time simulation results derived from a case study demonstrate the effectiveness of the model in illustrating the oligopoly in electricity marketplace and also the alternative decisions to attenuate it. Such a decision tool enables both the generation firms and the regulators to gain perfect insight into the oligopoly and its consequences in the long-term capacity investment in the electricity market. [ABSTRACT FROM PUBLISHER]

This paper studies the impact of natural gas (NG) contracts and constraints on a GENCO's midterm risk-constrained hydrothermal scheduling problem. The NG contracts and constraints are modeled as a set of linear equations. The proposed model utilizes the stochastic price-based unit commitment (PBUC). The PBUC hourly solution considers uncertainties of market prices for energy and ancillary services, uncertainties of natural water inflows, and random NG infrastructure interruptions in Monte Carlo scenarios. Illustrative examples analyze the GENCO's risk levels when considering midterm schedules for generating units, target payoffs, and usages of water inflow, NG and other thermal resources. Simulation results show that a GENCO's midterm schedules and financial risks could be impacted significantly with the consideration of NG contracts and constraints. [ABSTRACT FROM PUBLISHER]

This paper proposes a bilevel programming approach to determine the optimal contract price of dispatchable distributed generation (DG) units in distribution systems. Two different agents are considered in the model, namely, the distribution company (DisCo) and the owner of the DG. The former seeks the minimization of the payments incurred in attending the forecasted demand, while the latter seeks the maximization of his profit. To meet the expected demand, the DisCo has the option to purchase energy from any DG unit within its network and directly from the wholesale electricity market. A traditional distribution utility model with no competition among DG units is considered. The proposed model positions the DG owner in the outer optimization level and the DisCo in the inner one. This last optimization problem is substituted by its Karush-Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software. Tests are performed in a modified IEEE 34-bus distribution network. [ABSTRACT FROM AUTHOR]

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level a is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts. [ABSTRACT FROM AUTHOR]

One of the tasks of the system operator (SO) is to control the frequency of the system within defined limits. In order to do this, the SO has to provide enough power reserves which can be provided on the basis of bilateral contracts or on the relevant competitive market. In this paper, several methods for the power-reserve pricing of frequency control are presented. These methods are well suited to small power systems, where an insufficient number of potential bidders makes an ancillary-services market difficult to organize. The proposed pricing methods are meant for estimating the annual costs for power-reserves provision when these reserves are provided on the basis of bilateral contracts. The proposed pricing methods are applied to the Slovenian power system. [ABSTRACT FROM AUTHOR]

This work proposes a dynamic model to devise the optimal risk-averse investment policy in a portfolio of complementary renewable sources for a generation company in the Brazilian power system. The proposed method merges a static energy-contracting model, based on a hybrid robust-and-stochastic optimization approach, with a mean reverting binomial lattice model for real-option valuation. The proposed merge extends previous works by providing support to risk-averse investment decisions in complementary renewable sources dynamically distributed over time. The most important results of the model are: how much capacity to invest or build from each renewable source, how much to sell from the energy portfolio in bilateral contracts, and the optimal timing to invest. Unlike previous reported works, our model takes into account three classes of uncertainties simultaneously: renewable production of candidate sources and prices in the spot and contract markets. A case study with realistic data from the Brazilian power system is presented to illustrate the value of our model. [ABSTRACT FROM AUTHOR]

Efficient operation of electricity markets necessitates a penalty mechanism to penalize market participants who do not fulfill their market obligations. However, the traditional penalty mechanisms that have been successfully implemented in wholesale electricity markets may result in unintended consequences in transactive markets where the participants have uncertain supply or demand. In this regard, this letter investigates main challenges of applying traditional penalty mechanisms in transactive energy markets. Some efficient strategies are also proposed to operate the transactive markets in such a way that guarantees its security as well as liquidity. [ABSTRACT FROM AUTHOR]

Aggregation of diverse wind power sources can effectively reduce their uncertainty, and hence the cost of wind energy integration. A risky power contract is proposed, by which wind power producers (WPPs) can trade uncertain future power for efficient wind aggregation. A two-settlement market with both the risky power contract and a conventional firm power contract is shown to have a unique competitive equilibrium (CE), characterized in closed form. The marginal contribution and diversity contribution of each WPP to the group of all WPPs are fairly reflected in the profit earned by this WPP at the CE. Moreover, the CE achieves the same total profit as achieved by a grand coalition of WPPs. In a coalitional game setting, the profit allocation induced by the CE is always in the core, and is achieved via a non-cooperative risky power market. The benefits of the risky power market are demonstrated using wind generation and locational marginal price data for ten WPPs in the PJM interconnection. [ABSTRACT FROM PUBLISHER]