Decentralized Unit Commitment in Competitive Energy Markets

Problem statement: In a competitive electricity market with limited number of producers, Generation Companies (Gencos) is facing an oligopoly market rather than a perfect competition. Under oligopoly market environment, each Genco may increase its own profit through a favorable bidding strategy. The objective of a Genco is to maximize its profit and minimize the associated risk. In order to achieve this goal, it is necessary and important for the Genco to make optimal bidding strategies with risk management before bidding into spot market to get an expected high profit, since spot prices are substantially volatile. This study propose a method to build optimal bidding strategies in a day-ahead electricity market with incomplete information and considering both risk management and unit commitment. Approach: The proposed methodology employs the Monte Carlo simulation for modeling a risk management and a strategic behavior of rival. A probability density function (pdf), Value at Risk (VaR) and Monte Carlo simulation used to build optimal bidding strategies for a Genco. Results: The result of the proposed method shows that a Genco can build optimal bidding strategies to maximize expected total profit considering unit commitment and risk management. The Genco controls the risk by setting the confidence level. If the Genco increase the confidence level, the expected total VaR of profit decrease. Conclusions/Recommendations: The proposed method for building optimal bidding strategies in a day-ahead electricity market to maximize expected total profit considering unit commitment and risk management is helpful for a Genco to make a decision to submit bidding to the Independent System Operator (ISO).

This chapter revisits two commonly accepted assumptions about the economics of deregulated electricity markets. It first disproves that, in theory and under the condition of perfect information, decentralized and centralized unit commitment (UC) would lead to the same power quantities traded and the same optimal social welfare. It then shows that a generator owner’s optimum bid sequence for an auction market is generally above marginal cost, even where absolutely no abuse of market power is involved.KeywordsMarginal CostMarket PowerElectricity MarketUnit CommitmentSpot PriceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Optimizing biogas plants with excess power unit and storage capacity in electricity and control reserve markets

This paper presents a mathematical model of smart loads in demand response (DR) schemes, which is integrated into centralized unit commitment (UC) with optimal power flow coupled energy management systems for isolated microgrids for optimal generation and peak load dispatch. The smart loads are modeled with a neural network (NN) load estimator as a function of the ambient temperature, time of day, time of use price, and the peak demand imposed by the microgrid operator. To develop the NN-based smart load estimator, realistic data from an actual energy hub management system is used for supervised training. Based on these, a novel microgrid energy management system (MEMS) framework based on a model predictive control approach is proposed, which yields optimal dispatch decisions of dispatchable generators, energy storage system, and peak demand for controllable loads, considering power flow and UC constraints simultaneously. To study the impact of DR on the microgrid operation with the proposed MEMS framework, a CIGRE benchmark system is used that includes distributed energy resources and renewables based generation. The results show the feasibility and benefits of the proposed models and approach.

Growth in electricity demand with lack of investment in new transmission facilities has led to the formation of load pockets with long congestion hours over transmission bottlenecks. When assessing resource adequacy with reference to the load pockets, one needs to include both generation resources and power delivery systems to capture their impacts on system reliability and market economics at the delivery points. This paper presents a resource adequacy analysis approach to evaluate reliability as well as economic impact in a competitive energy market. Unavailability of generating units and transmission facilities is considered in market simulations to assess the overall adequacy of load-serving ability in a load pocket. Effects of transmission are illustrated through a market simulation analysis of the NYCA system based on publicly available transmission, generation, and demand data.

Mitigation of price spike in unit commitment: A probabilistic approach

Methods of participating power spot market bidding and settlement for renewable energy systems

This article aims to analyze agents’ behavior in a competitive hydrothermal energy market. The idea is to investigate how much the day-to-day behavior of the market can be different from the predictions presented by cost-based models because of the risk perception of each agent (hydroelectric energy producer, in this case) as a participant of the market. The main contribution is in determining the impact on the agents’ revenue in the short-term market due to the variation in the amount of energy generated and the market price, which other methodologies may not be able to capture. For this reason, a case study was made using daily simulations in a given month, observing the strategy and bids of eight hydroelectric agents for a central market operator emulated by an energy price offer simulator called SOPEE. The study reflected qualitative and quantitative examples of how the risk perception and the behavior of each agent can influence market behavior due to the variation in their perceptions of the parameters that form the energy price.

The paper describes a procedure developed to assist a generating company in choosing the most convenient bidding strategies for a day-ahead electricity energy market. According to the proposed method, the profit maximization problem is transformed into a minimization problem that can be solved by a traditional hydro-thermal unit commitment program after implementing a few modifications. The paper describes the modifications introduced in a unit commitment program based on the Lagrangian relaxation approach and on a disaggregated Bundle method for the solution of the dual problem. It also presents some results obtained for a realistic data set of hydrothermal power plants. The results are discussed in order to emphasize how the method can be applied to assess the bidding strategy choice of a given company.

Cloud providers are auctioning their excess capacity using dynamically priced virtual instances. These spot instances provide significant savings compared to on-demand or fixed price instances. The users willing to use these resources are asked to provide a maximum bid price per hour, and the cloud provider runs the instances as long as the market price is below the user's bid price. By using such resources, the users are exposed explicitly to failures, and need to adapt their applications to provide some level of fault tolerance. In this paper, we expose the effect of bidding in the case of virtual HPC clusters composed of spot instances. We describe the interesting effect of uniform versus non-uniform bidding in terms of both the failure rate and the failure model. We propose an initial attempt to deal with the problem of predicting the runtime of a parallel application under various bidding strategies and various system parameters. We describe the relationship between bidding strategies and programming models, and we build a preliminary optimization model that uses real price traces from Amazon Web Services as inputs, as well as instrumented values related to the processing and network capacities of cluster instances on the EC2 services. Our results show preliminary insights into the relationship between non-uniform bidding and application scaling strategies.

This paper presents an optimization-based method to model the mid-term operation of a hydrothermal power system in a competitive market. Two market structures are examined: perfect competition and oligopoly comprising a dominant firm with a competitive fringe. In the latter, the dominant firm bidding strategy is determined by solving a profit maximization problem; forward contracts are also modeled. Producers submit offers to a day-ahead market, which is cleared by the system operator on a bid-cost minimization basis. A yearly planning horizon with hourly time intervals is adopted. A real size hydrothermal system, similar to the Greek Power System, is used for our tests. Hourly thermal unit commitment and start-up / shut-down decisions, market clearing prices, hydroplant generation / pumping and reservoir volumes, monthly price duration curves, ISO payment and thermal producer profits are among the results obtained.

Extended opportunity cost model to find near equilibrium electricity prices under non-convexities

As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides a reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.

We present a new formulation to the unit commitment problem suitable for an electric power producer in a deregulated market and consider computationally efficient procedures to solve it. When the option of selling or buying electric power at spot market prices is included in unit commitment decisions, the optimal solution of a UCP with M units under standard operating and load constraints can be obtained by solving M uncoupled sub-problems. We account for the volatility of the spot market price of electricity by using a stochastic model. The model incorporates both the stochastic features of the availabilities of generating units participating in the market and the uncertainty of the aggregate load. We use probabilistic dynamic programming to solve the stochastic optimization problem. We show that for a market of 150 units the unit commitment problem can be accurately solved in a reasonable time by using the normal, Edgeworth, or Monte Carlo approximation methods for estimating the needed probability distributions.

Unit commitment (UC) along with economic load dispatch (ELD) is an integral optimisation problem in the power generation industry. In this paper, policy-based algorithms are developed to address dispatch problems under different system configurations with the benefit of providing flexibility in addressing several desired policies of decision-makers. A case study is conducted in a diesel-fired, power plant in central Philippines to elucidate the proposed approach. Three policies were created to effectively address the optimisation problem: 1) classic UC model; 2) buying and selling strategy; 3) continuous loading strategy with various issues being considered. Results show that the strategies exhibit a significant decrease in total costs and ease of implementation with respect to the current production schedule of the case firm. Furthermore, the second policy provides the least generated cost which implies that generating firms must consider the option to sell power when market prices reach desired levels.