Two-settlement Electricity Market Research Articles

Game-Based Optimal Aggregation of Energy Prosumer Community With Mixed-Pricing Scheme in Two-Settlement Electricity Market

Game-Based Optimal Aggregation of Energy Prosumer Community With Mixed-Pricing Scheme in Two-Settlement Electricity Market

Allocating Ex-post Deviation Cost of Virtual Power Plants in Distribution Networks

Virtual power plants (VPPs) including distributed generation, energy storage, and elastic load are emerging in distribution networks. Multiple VPPs can participate in electricity market as an aggregated entity and effective cost allocation mechanism among VPPs is a crucial issue. This paper focuses on allocating ex-post cost of VPPs incurred by deviation between actual power and ex-ante schedule in a two-settlement electricity market. We obtain approximate quadratic formulation of ex-post deviation cost considering network loss and develop an analytical cost allocation algorithm based on cooperative game theory. The allocated cost is consistent with cost causation principle and provides VPPs with incentive for aggregation. The proposed allocation method and relevant theoretical result are evaluated and verified by numerical tests.

Machine learning analytics for virtual bidding in the electricity market

In order to solve the problem of the high risks and low efficiency caused by the inconsistency of the day-ahead and real-time prices in two-settlement electricity market, virtual bidding is used to arbitrage on the difference between such two market prices that are unknown to virtual bidders to promote the price convergence. The problem of optimal bidding for virtual bidders from the spatio-temporal dimensions is addressed in this paper. The model takes the budget constraints of virtual bidders into account, as well as considers decrement and increment bids of virtual bidding to maximize the cumulative payoff of virtual bidders, which is formulated as a Markov Decision Process problem. Meanwhile, the conditional value-at-risk is used to quantify and hedge the risks faced by virtual bidders. A deep reinforcement learning algorithm is used to achieve an effective solution to the optimal bidding strategy problem through continuous interaction with a simulated building environment to obtain feedback and update the parameters of the neural network without referring to any prior model knowledge. The PJM data from 2016 to 2018 is used to calculate the cumulative profits and Sharpe ratio of virtual bidders. Compared with greedy algorithm and dynamic programming, the deep reinforcement learning algorithm is verified the effectiveness and superiority in this paper.

Dynamic operating reserve procurement improves scarcity pricing in PJM

Competitive electricity markets can procure reserve generation through a market in which the demand for reserves is administratively established. A downward sloping or stepped administrative demand curve is commonly termed an operating reserve demand curve (ORDC). We propose a dynamic formulation of an ORDC with generator forced outage probabilities conditional on ambient temperature to implement scarcity pricing in a wholesale electricity market. This formulation improves on common existing methods used by wholesale market operators to articulate ORDCs by explicitly accounting for a large source of observed variability in generator forced outages, whereby for a fixed load, more reserves are required during times of extreme heat and cold to maintain a constant risk of reserve shortage. Such a dynamic ORDC increases social welfare by $17.1 million compared to current practice in the PJM Interconnection during a high load week in a welfare-maximizing electricity market with co-optimized procurement of energy and reserves. A dynamic ORDC increases reserve prices under scarcity conditions, but has minimal effects on total market payments. The results are directly relevant to the modeled two-settlement electricity market in PJM, which is currently undergoing enhancements to its ORDC.

Economic and environmental implications of different approaches to hedge against wind production uncertainty in two-settlement electricity markets: A PJM case study

This paper examines the economic and environmental outcomes of four two-settlement electricity market clearing designs. The first design corresponds to a Deterministic Market Clearing (DMC) similar to the mechanism currently used in organized wholesale electricity markets in the United States. The other three designs account for the day-ahead (DA) wind power production uncertainty into the DA market mechanisms either implicitly or explicitly. An Augmented Deterministic Market Clearing (ADMC) design introduces DA ramp-capability products. These products ensure adequate and ramp-feasible electricity generation capacity commitments in the DA stage to cope with the real-time realization of wind power generation. A Hybrid Deterministic Market clearing (HDMC) design augments ADMC by explicitly integrating a characterization of wind power production uncertainty into the residual unit commitment (RUC) process, which is run after the DA market is closed, using stochastic programming. The last design, referred to as stochastic market clearing (SMC), uses stochastic optimization to explicitly account for wind power production uncertainty in the DA market clearing mechanisms (i.e. DA unit commitment and economic dispatch).The four market clearing designs are assessed by simulating the electricity market operations of a test system and comparing their results in terms of operating costs, prices, costs and revenues of different types of producers, consumer's payments, integration of wind power, and air emissions. The test system has 12% of the capacity of PJM's fossil-fired power generation fleet, and uses data on coincident demand and wind power production from the Bonneville Power Administration (BPA) system during years 2010–2014. The simulations are performed hourly for a whole year.Results show that SMC is superior as its costs reductions are more than two times the improvements attained by ADMC and HDMC. Also, SMC results in electricity prices that are better aligned with operation costs, cuts the spread between the day-ahead and real-time prices by >40%, reduces out-of-market short-term revenue sufficiency payments by 58%, reduces CO2 emissions by 3.52%, and decreases power plants' cycling. HDMC is a distant second-best market design. Relative to DMC, it achieves a reduction in total costs that is less than half the reduction achieved by SMC, a reduction in out-of-market payments that is 80% of the reduction attained by SMC, and an increase in wind power integration that is <10% the improvement obtained under SMC.

Enabling Active/Passive Electricity Trading in Dual-Price Balancing Markets

In electricity markets with a dual-pricing scheme for balancing energy, controllable production units typically participate in the balancing market as “active” actors by offering regulating energy to the system, while renewable stochastic units are treated as “passive” participants that create imbalances and are subject to less competitive prices. Against this background, we propose an innovative market framework whereby the participant in the balancing market is allowed to act as an active agent (i.e., a provider of regulating energy) in some trading intervals and as a passive agent (i.e., a user of regulating energy) in some others. To illustrate and evaluate the proposed market framework, we consider the case of a virtual power plant (VPP) that trades in a two-settlement electricity market composed of a day-ahead and a dual-price balancing market. We formulate the optimal market offering problem of the VPP as a three-stage stochastic program, where uncertainty is in the day-ahead electricity prices, balancing prices and the power output from the renewable units. Computational experiments show that the VPP expected revenues can increase substantially compared to an active-only or passive-only participation, and in the paper, we discuss how the variability of the stochastic sources affects the balancing market participation choice.

On the marginal value of electricity storage

We investigate the problem of characterizing the economic value of energy storage capacity to a wind power producer (WPP) that sells its energy in a conventional two-settlement electricity market. The WPP can offer a forward contract to supply power in the day-ahead market, subject to financial penalties for imbalances between the contracted power and the power that is delivered in real-time. We consider the setting in which the WPP has access to a co-located energy storage system, and can thus reshape its wind power production subject to its storage capacity constraints. Modeling wind power as a random process, we show that the problem of determining optimal forward contract offerings—given recourse with storage—is convex. We further establish that the maximum expected profit is concave and non-decreasing in the energy storage capacity, which reveals that the greatest marginal benefit from energy storage is derived from initial investment in small storage capacity. We provide a characterization of the marginal value of small energy storage capacity to the WPP. The formulae we derive shed light on the relationship between the value of storage and certain statistical measures of variability in the underlying wind power process.

Distributed Scalable Autonomous Market-Based Demand Response via Residential Plug-In Electric Vehicles in Smart Grids

Flexibility in power demand, diverse usage patterns and storage capability of plug-in electric vehicles (PEVs) grow the elasticity of residential electricity demand remarkably. This elasticity can be used to form the daily aggregated demand profile and/or alter instantaneous demand of a system wherein a large number of residential PEVs share one electricity retailer. In this paper, we propose a demand response technique to manage vehicle-to-grid enabled PEVs’ electricity assignments (charging and discharging) in order to reduce the overall electricity procurement costs for a retailer bidding to a two-settlement electricity market, i.e., a day-ahead and a spot or real-time (RT) market. We show that our approach is decentralized, scalable, fast converging and does not violate users’ privacy. Extensive simulations show significant overall cost savings can be achieved for a retailer bidding to an operational electricity market by using the proposed algorithm. This technique becomes more needful when the power grid accommodates a large number of intermittent energy resources. There, RT demand altering is crucial due to more likely contingencies and hence more RT price fluctuations and even occurring the so-called black swan events . Finally, such retailer could offer better deals to customers as well to stay competitive.

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then, we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a two-settlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.

Price-Taker Offering Strategy in Electricity Pay-as-Bid Markets

The recent increase in the deployment of renewable energy sources may affect the offering strategy of conventional producers, mainly in the balancing market. The topics of optimal offering strategy and self-scheduling of thermal units have been extensively addressed in the literature. The feasible operating region of such units can be modeled using a mixed-integer linear programming approach, and the trading problem as a linear programming problem. However, the existing models mostly assume a uniform pricing scheme in all market stages, while several European balancing markets (e.g., in Germany and Italy) are settled under a pay-as-bid pricing scheme. The existing tools for solving the trading problem in pay-as-bid electricity markets rely on nonlinear optimization models, which, combined with the unit commitment constraints, result in a mixed-integer nonlinear programming problem. In contrast, we provide a linear formulation for that trading problem. Then, we extend the proposed approach by formulating a two-stage stochastic problem for optimal offering in a two-settlement electricity market with a pay-as-bid pricing scheme at the balancing stage. The resulting model is mixed-integer and linear. The proposed model is tested on a realistic case study against a sequential offering approach, showing the capability of increasing profits in expectation.

Virtual Bidding: Equilibrium, Learning, and the Wisdom of Crowds

We present a theoretical analysis of virtual bidding in a stylized model of a single bus, two-settlement electricity market. North-American ISOs typically take a conservative approach to uncertainty, scheduling supply myopically in day-ahead (DA) markets to meet expected demand, neglecting the subsequent cost of recourse required to correct imbalances in the realtime (RT) market. This can result in generation costs that far exceed the minimum expected cost of supply. We explore the idea that virtual bidding can mitigate this excess cost incurred by myopic scheduling on the part of the ISO. Adopting a game-theoretic model of virtual bidding, we show that as the number of virtual bidders increases, the equilibrium market outcome tends to the socially optimal DA schedule, and prices converge between the DA and RT markets. We additionally analyze the effects of virtual bidding on social welfare and the variance of the price spread. Finally, we establish a repeated game formulation of virtual bidding, and investigate simple learning strategies for virtual bidders that guarantee convergence to the Nash equilibrium.

Dynamic Scheduling of Operating Reserves in Co-Optimized Electricity Markets With Wind Power

We propose a probabilistic methodology to estimate a demand curve for operating reserves, where the curve represents the amount that a system operator is willing to pay for these services. The demand curve is quantified by the cost of unserved energy and the expected loss of load, accounting for uncertainty from generator contingencies, load forecasting errors, and wind power forecasting errors. The methodology addresses two key challenges in electricity market design: integrating wind power more efficiently and improving scarcity pricing. In a case study, we apply the proposed operating reserve strategies in a two-settlement electricity market with centralized unit commitment and economic dispatch and co-optimization of energy and reserves. We compare the proposed probabilistic approach to traditional operating reserve rules. We use the Illinois power system to illustrate the efficiency of the proposed reserve market modeling approach when it is combined with probabilistic wind power forecasting.

Nash Equilibrium in a two-settlement electricity market using competitive coevolutionary algorithms

This paper investigates the use of competitive coevolutionary algorithms to calculate suppliers’ optimal strategies in a deregulated electricity market. The two settlement model is used, consisting of a spot and a forward market. Agents can take part in both spot and forward transactions, and act strategically to maximise their profit from both markets. The strategic interactions of market agents are modelled as a non cooperative game. The competitive coevolutionary Algorithm is used to calculate the Nash Equilibrium strategies ensuring the best outcome for each agent. Results demonstrates the effectiveness of the coevolutionary approach to find the optimal strategies in different market situations.

Analyzing Two-Settlement Electricity Market Equilibrium by Coevolutionary Computation Approach

Forward contracts play an important role for market power mitigation and risk hedging in electricity markets. In this paper, a two-settlement electricity market including a forward market and a spot market is formulated as a two-stage game. The linear supply function equilibrium (LSFE) model and Cournot model are used to model strategic bidding for the spot market, while the forward market is modeled by Cournot model. A coevolutionary genetic algorithm (CGA) is employed to determine the market equilibrium. The paper then examines the question whether generation companies (GenCos) would voluntarily enter forward markets due to economic inspiration and studies theoretically and numerically the factors which can affect the bidding behaviors of GenCos. The effectiveness of CGA in determining the market equilibrium is investigated and demonstrated based on two test examples. The studies show that GenCos' decisions on the participation in forward markets depend significantly on the type of competition that they have in the spot market. Moreover, GenCos' bidding behaviors in the forward market can be affected by their cost parameters and the slope of system demand function significantly.