Since the inception of competitive power markets two decades ago, electricity price forecasting (EPF) has gradually become a fundamental process for energy companies' decision making mechanisms. Over the years, the bulk of research has concerned point predictions. However, the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply, demand and prices. Academics and practitioners alike have come to understand that probabilistic electricity price (and load) forecasting is now more important for energy systems planning and operations than ever before. With this paper we offer a tutorial review of probabilistic EPF and present much needed guidelines for the rigorous use of methods, measures and tests, in line with the paradigm of 'maximizing sharpness subject to reliability'. The paper can be treated as an update and a further extension of the otherwise comprehensive EPF review of Weron (2014, IJF) or as a standalone treatment of a fascinating and underdeveloped topic, that has a much broader reach than EPF itself.

Recent advances in electricity price forecasting: A review of probabilistic forecasting

A stochastic-robust coordinated optimization model for CCHP micro-grid considering multi-energy operation and power trading with electricity markets under uncertainties

A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets

To realize the win-win benefits and resource coordination of the multilevel operating entities of a “microgrid cluster (MGC), microgrid (MG) and user” and improve the self-consumption of new energy in the MGC, this paper proposes an energy trading model and solution algorithm of an “MGC, MG and user” based on a multi-agent-system, incentive demand response, and hierarchical Stackelberg game theory. By analyzing the game objectives and strategies of these participants, the unique Stackelberg equilibrium (SE) of the hierarchical Stackelberg game is proved theoretically. The game optimization process is divided into two levels. In the upper-level game, the MGC as a leader stimulates the MG to participate in intracluster dispatching by establishing an internal price incentive mechanism. As the follower, the MG determines the number of electricity transactions based on the realized internal price to maximize its own profits. In the lower-level game, the MG leads the game by deciding electricity selling prices based on the load demands of users, and the user as follower adjust electricity consumption using strategies to balance expenditure and experience of electricity usage. Simulation results verified the effectiveness and good convergence of the proposed method and demonstrated that the proposed hierarchical game strategy can improve the economic benefits of each participant, which is conducive to the establishment of friendly grid-connected MGC.

Energy Management Optimization of Microgrid Cluster Based on Multi-Agent-System and Hierarchical Stackelberg Game Theory

Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach

Because of severe uncertainty and poor dispatchability of renewable energy, when renewable generators (RGs) bid in the day-ahead (DA) wholesale electricity market, how to tackle the deviations between their actual outputs and cleared quantities from the market is one of the important topics in the design of electricity markets with the RGs. In this paper, a demand response exchange (DRX) market is introduced to counterbalance the deviations, where both upward and downward DR resources are considered and the DRX market price is determined by the bids of DR customers. In addition, to examine impacts of the DRX market on strategic behaviors of participants in the DA market, a joint equilibrium model of the DA market and the DRX market is proposed. Specifically, in the DRX market, DR customers compete by submitting their bids in the form of supply functions when the RGs’ total actual output is less than the cleared quantity from the DA market, while the demand function bid mode is applied for DR customers when the RGs’ total actual output is greater than the cleared quantity from the DA market. Monte Carlo simulation and scenario reduction techniques are employed to deal with uncertainties of the actual output of RGs. Moreover, the equilibrium model is converted into a convex optimization problem for the solution, and the existence and uniqueness of Nash equilibrium for the DA market and DRX market are theoretically demonstrated. Due to information asymmetry in practice, a distributed algorithm is applied to find equilibrium outcomes. Finally, numerical examples are presented to verify the reasonableness and effectiveness of the proposed model and algorithms.

Equilibrium Analysis of Electricity Market With Demand Response Exchange to Counterbalance Bid Deviations of Renewable Generators

Deregulation of electricity retail market enables competition among retailers and thereby enriches customers’ choices. In this environment, retailers compete in the retail market mainly by bidding their retail prices. As bidding price of a retailer increases, customers would reduce their demands on this retailer, or even switch their demands to other retailers with lower bidding prices. These potential consequences will inevitably impact retailers’ bidding strategies in the retail market. The impacts of consumers’ switching behaviors and contract trading on strategic behaviors of retailers in the retail market is first investigated in this paper. A Bertrand-based game model for the retail market is proposed while considering customers’ switching behaviors and retailers’ contract trading. Specifically, in the proposed model, the market share function is introduced to describe customers’ elasticity and switching behaviors. Also, the close-loop interaction of the wholesale market and retail market is considered. In addition, the existence and uniqueness of the Nash equilibrium for the game model are proved. The nonlinear complementarity approach is employed to find the Nash equilibrium outcomes. Effectiveness of the theoretical model is verified by numerical examples. Simulation results show that both customers’ switching behaviors and retailers’ contract trading can help mitigate market power abuse of retailers.

Game Analysis of Electricity Retail Market Considering Customers’ Switching Behaviors and Retailers’ Contract Trading

Microgrid is an effective way to accommodate distributed renewable energy, and there is a need for microgrids to participate in electricity market competition to ensure its sustainable development. For this purpose, a market trading framework is presented where microgrids sell electricity by submitting bids in the distribution electricity market (DEM) while generators compete by submitting bids in the day-ahead wholesale market (DAWM). The retailers are considered to submit bids in the two markets to buy electricity to meet the demand of customers and an arbitrageur is introduced to buy and sell electricity between the DEM and the DAWM. Based on the market framework, a joint equilibrium model for the DEM and the DAWM is proposed. Moreover, the equilibrium problem is converted into a convex optimization problem, and the existence and uniqueness of Nash equilibrium for the DWAM and the DEM is theoretically demonstrated. Due to information asymmetry in practice, a distributed algorithm is applied to find equilibrium outcomes. Finally, numerical examples are presented to verify the effectiveness of the proposed model and algorithm.

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Equilibrium Analysis of Electricity Markets With Microgrids Based on Distributed Algorithm

This paper investigates the problem of how to deploy customers’ shiftable load (SL) for electricity retailers’ risk management under uncertainty of the day-ahead (DA) wholesale market price. The robust profit (RP) and the conditional robust profit (CRP) are introduced for a risk-averse retailer’s risk-reward trade-off analysis in its decision-making of electricity procurement from various options. A CRP-based bi-level optimization model is proposed for the risk-averse retailer to determine its electricity procurement strategy taking into consideration customers’ shiftable load. In the upper problem, the retailer decides its electricity procurement from various options and the SL incentive prices to maximize its CRP under a given confidence level, and in the lower problem, the customers shift their load according to the SL incentive prices to minimize their comprehensive costs including the discomfort cost caused by rescheduling electricity consumption. Finally, a case study is used to verify the effectiveness of this model. It is shown that the retailer can achieve larger profit and less risk by utilizing customers’ SL and the retailer’s risk-aversion level has an important impact on its electricity procurement and SL incentive strategies.

https://www.mdpi.com/1996-1073/13/6/1308/pdf

Xian Wang

Papers

Equilibrium Analysis of Electricity Market With Demand Response Exchange to Counterbalance Bid Deviations of Renewable Generators

Game Analysis of Electricity Retail Market Considering Customers’ Switching Behaviors and Retailers’ Contract Trading

Equilibrium Analysis of Electricity Markets With Microgrids Based on Distributed Algorithm

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Probabilistic evaluations on marginal price and capacity adequacy of power systems with price-elastic demand