Pool-based Electricity Market Research Articles

Two-Stage Distributionally Robust Strategic Offering in Pool-Based Coupled Electricity and Gas Market

Two-Stage Distributionally Robust Strategic Offering in Pool-Based Coupled Electricity and Gas Market

Evaluating the cost of emissions in a pool-based electricity market

This paper proposes a methodology that quantifies the impact of emissions cost on the electricity price, the generation scheduling outcome, and the overall emissions in a pool-based electricity market. It employs a mid-term (yearly) generation scheduling model, by sequentially solving a Day-Ahead Scheduling problem, in which power plants internalize their emissions cost in the energy offers that they submit to the day-ahead market. It further explores several scenarios with respect to gas and carbon prices, as well as the realization of random outages that affect the availability of the generation units. An illustration on an instance of the Greek electricity market during the European Union Emissions Trading Scheme Phase III indicates that: (i) A carbon price equal to 30 €/tCO2e combined with low gas prices results in a 18.7% reduction of CO2 emissions, due to the substitution of lignite units by gas units in the energy generation mix. (ii) An increase in the carbon price by 1 €/tCO2e results in an increase of the weighted average electricity price ranging from 0.52 to 0.61 €/MWh. (iii) The average pass-through rate of the carbon costs onto the demand-side payments for carbon prices up to 15 €/tCO2e is close to 60%.

A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators

Large-scale renewable energy suppliers and electric vehicles (EVs) are expected to become dominated participants in future electricity market. In this article, a competitive bidding strategy is formulated for wind power plants (WPPs) and EV aggregators in a pool-based day-ahead electricity market. A bilevel multiagent based model is proposed to study their bidding behaviors, with market clearing completion in the lower level and revenue maximization in the upper level. A stochastic framework is developed to incorporate the uncertainties in maximal power production of WPPs and EV aggregators and bid prices of other participants. The process of bidding decision is formulated as a stochastic game with incomplete information, in which electricity suppliers including WPPs and EV aggregators are considered as players of the game, their lack of information in this stochastic market environment is counterbalanced by a multiagent reinforcement learning algorithm named win or learn fast policy hill climbing (WoLF-PHC) with maximizing their own profits by self-game. The feasibility and effectiveness of the proposed model and the WoLF-PHC solution approach are successfully illustrated using a modified IEEE 6-bus system and a modified 118-bus system with different numbers of market players.

A decision-making model for a retailer considering a new short-term contract and flexible demands

Electricity retailers encounter several uncertainties in a pool-based electricity market, such as uncertainties in demand and market price. This paper presents a new stochastic model for an electricity retailer with flexible demands to facilitate its energy procurement. The retailer, in addition to participating in the wholesale market, can exchange with a wind producer through a contract under the new short-term trading mechanism. In order to facilitate energy and financial exchanges, to estimate the spot market price, and to send contract information to system operator, the short-term trading operator is introduced. Following the day-ahead market clearing, the wind producer and the retailer submit their decisions on the contract to the introduced operator. The operator allocates possible energy transactions based on the surpluses or shortages of the parties. The uncertainty associated with market prices, demand, and the exchangeable power within short-term trading mechanism are considered by sets of scenarios. Also, profit risk is considered by conditional value at risk. The resulting stochastic linear formulation is applied to two case studies to show the effectiveness of the proposed model. Results indicate that the proposed mechanism increases the profit of the retailer during the short-term operation.

A Column-and-Constraint Generation Algorithm to Find Nash Equilibrium in Pool-Based Electricity Markets

Equilibrium analysis is crucial in electricity market designs, with Nash equilibrium recognized as the most powerful one. Its most prominent hindrance, however, is an efficient methodology to compute an equilibrium point in large-scale systems. In this work, a Column-and-Constraint Generation (CCG) algorithm is proposed to tackle this challenge. More precisely, the master problem finds a candidate for Nash equilibrium and the oracle identifies whether this candidate point is indeed an equilibrium. A set of numerical experiments was conducted, comparing its computational performance with the solution of an Equilibrium Problem with Equilibrium Constraint (EPEC). We identify that the proposed algorithm overcomes the benchmark in the magnitude of 20 times on average and more than 30 times in the most demanding instances. Furthermore, the scalability of the EPEC formulation is challenged even for medium-scale instances, whilst the proposed algorithm was able to handle all tested instances in a reasonable computational time.

A bi-level model for strategic bidding of a price-maker retailer with flexible demands in day-ahead electricity market

In this paper, a bi-level Stackelberg-based model between an electricity retailer and consumers is presented, in which the upper-level consists of a price-maker retailer (PMR) modeled as the leader who seeks to maximize its own profit by adopting optimal pricing strategies for a pool-based electricity market. At the same time, PMR reduces its risks by encouraging consumers to actively participate in demand response (DR) programs. The lower-level of the model consists of 4 followers, three of them represent customer groups with distinct reactions to DR programs, and their objective function is defined as minimizing the cost of purchased electricity while preserving the welfare level. The fourth follower is the electricity pool, which is responsible for implementation of market mechanism and determination of market clearing price (MCP) with the aim of increasing the consumers’ welfare. In the proposed framework, the reaction of consumers to prices and DR programs are also organized and studied in form of several scenarios. The outputs of the proposed model will be enhancing the retailer’s profit and determination of the effect of DR programs on power consumption during peak hours as well as consumers’ welfare.

Exploring the Profitability and Efficiency of Variable Renewable Energy in Spot Electricity Market: Uncovering the Locational Price Disadvantages

While variable renewable energy (VRE) has been developed for decades, VRE market participation is developing relatively slowly, despite the potential economic efficiency it may bring. This paper tries to specify the efficiency of VRE in a deregulated pool-based electricity market. Based on standard pool-based market design, this paper built a direct current optimal power flow (DC-OPF) based simplified 2-settlement spot electricity market model conjugating electricity and ancillary service clearing. To address the outcomes of the imperfect market in the real world, this paper studied the consequences brought by agents’ learning and strategic behaviors. Simulations under different ancillary service levels and reliability cost levels are carried out. The results show that VRE may be unprofitable in the market, especially when learning and strategic behavior is considered. Learning and strategic market behavior will also hamper the role of VRE as a “better” energy source. This paper shows and proves a locational marginal price (LMP) disadvantage phenomenon, which will lead to low profitability of VRE. Three major suggestions are given based on the results.

Circuit-theory-based method for transmission fixed cost allocation based on game-theory rationalized sharing of mutual-terms

This paper proposes a new method to allocate the transmission fixed costs among the network participants in a pool-based electricity market. The allocation process relies on the circuit laws, utilizes the modified impedance matrix and is performed in two individual steps for the generators and loads. To determine the partial branch power flows due to the participants, the equal sharing principle is used and validated by the Shapley and Aumann-Shapley values as two preferred game-theoretic solutions. The proposed approach is also applied to determine the generators’ contributions into the loads, and a new concept, named circuit-theory-based equivalent bilateral exchange (EBE), is introduced. Using the proposed method, fairly stable tariffs are provided for the participants. Cross-subsidies are reduced and a fair competition is made by the proposed method due to the counter-flows being alleviated compared with the well-known Z-bus method. Numerical results are reported and discussed to validate the proposed cost allocation method. Comparative analysis reveals that the method satisfies all conditions desired in a fair and efficient cost allocation method. Finally, the developed technique has been implemented successfully on the 2383-bus Polish power system to emphasize that the method is applicable to very large systems.

A Promising Scheme for Portfolio Selection to Gain Pragmatic Pool-based Electricity Market Returns under Uncertain Circumstances

A Promising Scheme for Portfolio Selection to Gain Pragmatic Pool-based Electricity Market Returns under Uncertain Circumstances

Optimal Location of WT based Distributed Generation in Pool based Electricity Market using Mixed Integer Non Linear Programming

In this paper, analysis a Mixed Integer Nonlinear Programming (MINLP) approach has been utilized for determining optimal location and number of distributed generators considering minimization of fuel cost of conventional and Wind turbine power. The pattern of nodal real and reactive power prices have been obtained with and without Wind turbine integration. The results are also obtained for, loss reduction, fuel cost saving and voltage profile. The impact of different load models as PQ load and Zip load model has been studied. The proposed MINLP based optimization approach has been applied for IEEE24 bus reliability test system.

Price impact assessment for large-scale merchant energy storage facilities

This paper analyzes the impact of an independently-operated large-scale energy storage system on the electricity prices of a fully competitive pool-based electricity market. From a consumer's perspective, the impact of storage operation on energy cost is investigated. The changes in the revenue of generation units and entities caused due to the arbitrage operation of storage facility are also explored. In the current study, we have considered an optimization-based scheme to schedule the operation of a large-scale storage system. Also, a number of ad-hoc strategies to participate in the energy market are examined for the sake of comparison. Modeling the impacts of a large-scale energy storage system can inform planners and operators of the potential effects of storage on the rest of the system and help them to use storage most effectively. These analyses can also inform regulators and policy-makers of the potential societal costs and benefits to energy storage deployments that are not necessarily monetizable by the energy storage owner.

Finding Equilibria in the Pool-Based Electricity Market With Strategic Wind Power Producers and Network Constraints

This paper proposes a model to find the equilibria in the short-term electricity market with large-scale wind power penetration. The behavior of each strategic player is modeled through a two-stage mathematical problem with equilibrium constraints (MPEC), where the upper-level problem maximizes the profit of the strategic player and the lower-level problem describes the clearing processes of the day-ahead and real-time markets while considering the network constraints. The joint solution of all the MPECs constitutes an equilibrium problem with equilibrium constraints (EPEC). The uncertain wind power production and demand are represented by a set of plausible scenarios. By using the duality theory and Karush–Kuhn–Tucker condition, each MPEC is transferred into a mixed-integer linear programing problem. The Nash equilibria of the electricity market are obtained by solving the EPEC using Game theory and the diagonalization algorithm. Case studies are performed to show the effectiveness of the proposed model.

Real and Reactive Power Nodal Pricing with Hydro condenser in Pool Based Electricity Market Model

Reactive power procurement has been identified as an important ancillary service. The cost characteristics must be able to recover the lost opportunity cost component of generator reaching the field limits and causes the decrease in its active power when the reactive demand in the system increases. The Synchronous condensers supply reactive power to maintain the voltage and reduce the burden on the synchronous generators. In this paper, the cost characteristics of reactive power support by the generator are proposed based on its capability chart. A non-linear optimization problem formulation is proposed to compute both real and reactive power nodal prices for pool electricity market model with hydro condenser. The marginal prices at each node have been determined for both real and reactive power considering the minimization of fuel cost, reactive power cost, and cost of condenser. The cost characteristics of reactive power have been determined based on the existing methods and the proposed method. The comparison of the results obtained with existing method is also provided. The results have been determined for IEEE 24 bus test system.

Optimal rescheduling of real power generation for congestion management using teaching-learning-based optimization algorithm

This paper proposes teaching-learning-based optimization (TLBO) algorithm for congestion management (CM) in a pool based electricity market. Congestion is a principal problem that an independent system operator faces in post deregulated era. The aim of employing TLBO algorithm is to effectively relieve congestion in the line with minimum deviation in initial generation and, hence, congestion cost. Various security constraints such as load bus voltage and line loading are taken into account while dealing with this problem. Inspired by teaching–learning process of classroom, TLBO algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. It only requires common control parameters like population size and number of generation. In this paper, the proposed TLBO algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the TLBO algorithm for obtaining higher quality solution is also established.

A directional-based branches current method for transmission loss allocation in the pool-based electricity market

This paper proposes a new method for transmission loss allocation. The share of each bus in the transmission line losses is determined using transmission line loss equations with respect to bus-injected currents. Then, it is applied to the total network transmission lines. In the proposed method, comparing with other methods, a solution to remove the negative loss allocation has been introduced. This algorithm is based on the electric network relations and the injected power in various buses considering the network topology. The proposed method is studied on a typical three-bus network, and applied to the IEEE 14-bus networks. In comparison with other methods, a new solution for removing negative loss allocation is proposed.

Optimal Location of PV based Distributed Generation in Pool based Electricity Market using Mixed Integer Non Linear Programming

Optimal Location of PV based Distributed Generation in Pool based Electricity Market using Mixed Integer Non Linear Programming

Risk‐constrained multi‐level framework for coordination of generation and transmission expansion planning in liberalised environments – part I: theory and formulation

In this two-article set, a new multi-level framework is presented for coordination of generation and transmission expansion planning (G&TEP) in liberalised electricity markets. Part I is mainly dedicated to mathematical modelling of the G&TEP problem. The main aims of the proposed framework are not only to develop a holistic model for the G&TEP problem, but also to evaluate impacts of expansion plans on market participants’ strategic behaviours. To do so, the proposed methodology is decomposed into a planning master problem and an operating slave problem. In the slave problem, the first level indicates market participants’ strategic interactions and the second level represents a pool-based electricity market. In the master problem, an independent system operator model is described to coordinate decentralised generation expansion planning in the third level with centralised transmission expansion planning in the fourth level. In addition, the offered framework uses a well-founded information-gap decision theory to minimise risks of planning arising from severe uncertainties. The newly developed framework is formulated as a non-convex mixed-integer non-linear optimisation problem. Hence, a novel two-stage multi-dimensional melody search algorithm is widely employed to determine the best solution. The profitableness of the developed framework is illustrated in part II of this two-article set.

Strategic multiyear transmission expansion planning under severe uncertainties by a combination of melody search algorithm and Powell heuristic method

In this paper, a new strategic multiyear model is presented for Transmission Expansion Planning (TEP) in deregulated environments. This methodology is based on a tri-level decision making whose fundamental elements are pool-based electricity market and strategic behavior of market participants. In addition, to minimize risks of planning arising from severe uncertainties, an information gap decision theory (IGDT) is used. By using the IGDT, the TEP model is formulated for the risk-averse and risk-seeker decision makers through the robustness and opportunity models, respectively. The offered model is formulated as a non-convex mixed-integer non-linear optimization problem. With this regards, a combination of melody search algorithm and improved Powell heuristic method is widely used to determine the optimal solution. The planning methodology has been applied to the IEEE 30-bus test system and to the large-scale Iranian 400-kV transmission grid. Simulation results demonstrate the feasibility and effectiveness of the proposed model, and the fact that it can be profitable for the real-world networks.

A Stochastic Framework for Multi-stage Generation Expansion Planning under Environmental and Techno-economic Constraints

In this article, a new stochastic framework is proposed for multi-stage generation expansion planning subject to environmental/techno-economic constraints. In this framework, with a new point of view, impacts of expansion plans on strategic behaviors of both generation companies and distribution companies are evaluated. To do so, the proposed framework is developed as a three-level model. The lower level indicates a pool-based electricity market, the intermediate level provides strategic interactions among all market participants, and the upper level represents the generation expansion planning by considering the outcomes of the previous levels. To minimize risks of planning arising from severe uncertainties, a well-founded information gap decision theory is used to model both risk-averse and risk-taker decision makers. The newly developed model is a non-convex non-linear mixed-integer optimization problem. A new melody search algorithm and an innovative augmented Powell heuristic method are employed to obtain the final optimal solution. The feasibility and capabilities of the proposed methodology are illustrated on the 46-bus south Brazilian electric power grid. The obtained results from case studies validate the profitability of the newly developed framework.

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.