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We investigate the performance of two hybrid forecasting models to predict the next-day base load electricity prices on the APX power exchange for Great Britain. Hybrid models have often been advocated as a synthetic method for forecasting, but it is an open question how well they perform at the day-ahead stage. A conventional hybrid approach combines a fundamental model, formulated with supply stack modeling, with an econometric model using data on price drivers. We extend this model to one based upon logistic smooth transition regression, which represents a regime-switching for periods of structural change. Empirical results for out-of-sample forecasting using both hybrid approaches are discussed and compared to non-hybrid time series forecasting methods. [ABSTRACT FROM PUBLISHER]

Abstract: Electricity market simulators have become a useful tool to train engineers in the power industry. With the maturing of electricity markets throughout the world, there is a need for sophisticated software tools that can replicate the actual behavior of power markets. In most of these markets, power producers/consumers submit production/demand bids and the Market Operator clears the market producing a single price per hour. What makes markets different from each other are the bidding rules and the clearing algorithms to balance the market. This paper presents a realistic simulator of the day-ahead electricity market of mainland Spain. All the rules that govern this market are modeled. This simulator can be used either to train employees by power companies or to teach electricity markets courses in universities. To illustrate the tool, several realistic case studies are presented and discussed. [Copyright &y& Elsevier]

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed. [ABSTRACT FROM AUTHOR]

Pool-based electricity markets can be simulated with various degrees of accuracy. When compared to actual markets, most of the simulators produce outcomes than cannot be extrapolated beyond the specific scenario analyzed. This is most critical for regulators and market participants which need tools to analyze market power and bidding strategies, respectively, for a broad range of scenarios. Both objectives can be tackled if the possible equilibria of a poll-based multiperiod market are determined. This paper presents a three-step methodology to find these equilibria. First, a detailed model of an electricity market is presented, considering multiperiod bidding, price elasticity, and network modelling. Second, an iterative simulation process is run to detect participants' bidding strategies implicit in the optimized production resulting from the simulation. Finally, output data from the simulator are analyzed to obtain Nash equilibria. Iterated deletion is used in the last step to remove strategies that are dominated by others that generate higher profits. A realistic case study Illustrates the proposed. technique. [ABSTRACT FROM AUTHOR]

A numerical method based on a relaxation algorithm and the Nikaido-Isoda function is presented for the calculation of Nash-Cournot equilibria in electricity markets. Nash equilibrium is attained through a relaxation procedure applied to an objective function, the Nikaido-Isoda function, which is derived from the existing profit maximization functions calculated by the generating companies. We also show how to use the relaxation algorithm to compute, and enforce, a coupled constraint equilibrium, which occurs if regulatory, generation, and distribution (and more) restrictions are placed on the companies and entire markets. Moreover, we use the relaxation algorithm to compute players' payoffs under several player configurations. This is needed for the solution of our game under cooperative game theory concepts, such as the bilateral Shapley value and the kernel. We show that the existence of both depends critically on demand price elasticity. The numerical method converges to a unique solution under rather specific but plausible concavity conditions. A case study from the IEEE 30-bus system, and a three-bus bilateral market example with a dc model of the transmission line constraints are presented and discussed. [ABSTRACT FROM AUTHOR]

Recently, with the penetration of numerous Distributed Energy Resources (DER) in Smart Distribution Networks (SDN), Local Transactive Markets have emerged. Exchanging energy between all participants of local markets results in the satisfaction of producers and consumers. Based on these issues, this study provides a novel framework for the participation of SDN-independent entities in wholesale and local electricity markets simultaneously. In this regard, the considered system's players, namely Distribution System Operator (DSO) and DER Aggregator (AG), take part within local as well as wholesale markets in two-day ahead and real-time stages. Moreover, to deal with the inherent conflict between the existing players' interests, a Stackelberg game-based technique is proposed. In the raised competition, the leader, DSO, attempts to minimize its operating costs, while the follower, DER AG, tends to maximize its profit. Therefore, actors' actions choices within both markets are made non-cooperatively. On the other hand, to handle the uncertain nature of stochastic parameters in the depicted problem, Monte Carlo Simulation (MCS), together with a fast backward/forward scenario reduction approach, is exploited. Ultimately, to evaluate the efficiency of the proposed scheme, two different case studies, with and without considering the competitive environment, are implemented on a modified IEEE-33 bus SDN. [ABSTRACT FROM AUTHOR]

The electricity market liberalization propelled by the increased operational flexibility observed in distribution systems urges the development of mathematical models for competitive electricity markets accounting for the participation of distribution systems operators. Most existing approaches model the interaction between the agents and the market as a bilevel programming problem, wherein the market is characterized at the lower level. Hence, the upper-level agent's objective function is favored over minimizing the costs of supplying the system's demand. In this paper, a new bilevel formulation is proposed. In the upper level, the market operator determines the optimal energy price that ensures the demand supply and maximizes the overall social welfare. In the lower level, the generation companies and distribution systems operators determine the amount of energy injected/extracted into/from the transmission system to maximize profits/minimize operational costs. A new pricing method is proposed in this paper as an alternative to the transmission system's dual variables, which are inaccessible under the proposed framework. The resulting nonlinear mixed-integer bilevel programming formulation is transformed into an equivalent single-level mixed-integer linear program. Numerical results illustrate the proposed approach's effectiveness in maintaining the minimum operational costs while distributing the payoff amongst the agents. • Bilevel formulation for wholesale markets with non-strategic price-maker agents. • New market framework with a clearing process that minimizes the total costs. • Development of a new pricing mechanism for electricity markets. • Energy storage systems and demand response minimizes the clearing prices. [ABSTRACT FROM AUTHOR]

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented. [ABSTRACT FROM PUBLISHER]

This paper presents a stochastic framework for offering and bidding strategies of a hybrid power generation system (HPGS) with a wind farm and two types of energy storage facilities, i.e., compressed air energy storage (CAES) and battery energy storage (BES) systems. The model considers the participation of the HPGS in consecutive electricity markets, i.e., day-ahead (DA) and intraday markets. To better address the proposed trading strategy problem, the BES degradation cost is also incorporated into the model. Furthermore, a mechanism based on energy procurement from demand response resources (DRRs) in the intraday demand response exchange (IDREX) market for the HPGS is also established to offset unexpected energy imbalances effectively. The suggested offering and bidding strategy is formulated as a three-stage stochastic programming problem incorporating a risk-alleviating index, namely, the conditional value-at-risk (CVaR). Results from several simulations indicate considerable profit gain and risk reduction achieved by the suggested offering and bidding framework. • Operating model of a wind-compressed air energy storage-battery system is developed. • The impact of the intraday demand response exchange market is thoroughly analyzed. • Role of battery degradation cost in joint and disjoint operating models is studied. • A three-stage scenario-based architecture is suggested to cope with the uncertainties. • Joint operating strategy ensures economic betterment compared to the disjoint one. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid power generation company consisting of a concentrated solar power unit, wind turbines, a battery system, and a demand response provider is established to take part in electricity markets. The operating strategy of the hybrid power generation company in day-ahead and adjustment (intraday) markets is determined based on their coordinated operation. To tackle the intrinsic uncertainties, for the first time, a mixed stochastic-interval model is proposed which addresses the uncertainty in demand response and solar energy via interval optimization. The examined problem is formulated as a multi-objective optimization problem in which the risk of both stochastic and interval parameters can be involved. On this basis, the proposed operating strategy covers three objective functions, namely, expected radius and midpoint of the hybrid power generation company's profit together with the conditional value-at-risk. Accordingly, the normal boundary intersection and lexicographic optimization techniques are utilized to derive feasible solutions. Lastly, numerical results are presented and the performance of the proposed framework is investigated. The results indicate that the suggested model can be efficiently used to handle the decision-maker's preference over interval and stochastic parameters, and the risk criterion associated with interval parameters becomes larger as the forecasting errors increase. • Defining a new hybrid power generation company participating in electricity markets. • Developing an operating strategy problem based on a mixed interval-CVaR model. • Modeling uncertainty of solar energy and demand response provider by interval numbers. • Establishing a tri-objective risk-involved programming problem. [ABSTRACT FROM AUTHOR]

This paper proposes a novel bi-level strategic energy trading framework to minimize the operation cost of the distribution network (DN) interacting with peer-to-peer (P2P) transactive energy hubs with electric vehicles. A distribution system operator at the upper level minimizes its total cost from purchasing electricity in the wholesale market, generating with its own microturbines, and selling electricity to the energy hubs. Each transactive energy hub at the lower level reacts to the offer price received from the upper level, interacting with the other energy hubs. Each energy hub has a parking lot to harvest the benefit from asynchronous storage of electricity in other energy hubs stemming from the difference between the arrival or departure times of the electric vehicles. A single-leader multi-follower game approach is developed to model the DN-energy hubs game structure. Then, an iterative model is proposed to find the equilibrium point between the leader and the followers, while the distributed problem of the interaction between the followers at the LL is solved by the Alternating Direction Method of Multipliers (ADMM). Numerical results for the IEEE 33-bus test system with two energy hubs show the effectiveness of the proposed transactive model between the energy hubs and the DN. • Proposing a game theory framework for distribution network optimization. • Proposing a bi-level approach for distribution network and transactive energy hubs. • Integrating electric vehicles in energy hubs to earn profit from trading electricity. • Presenting an uncertainty budget model for locational marginal prices. • Using a decentralized method to coordinate the exchange between energy hubs. [ABSTRACT FROM AUTHOR]

Hybrid power plants (HPPs) integrating dispatchable and non-dispatchable generation are gaining attention by generation companies due to their increased flexibility in the operation of the power system. In this paper, an offering and bidding framework for an HPP consisting wind, photovoltaic (PV), compressed air energy storage (CAES), battery energy storage (BES), and thermal units in day-ahead (DA) and intraday markets is presented. Moreover, the interaction between the HPP and demand response providers (DRPs) through the intraday demand response exchange (IDREX) market is incorporated into the proposed model. The existing uncertainties such as DA, intraday, imbalance prices, along with renewable energy and IDREX market, are tackled via a hybrid stochastic-interval approach. The suggested structure is not only capable of handling both stochastic and interval uncertainties but also can manage the risk associated with both uncertainty characterization methods. To this end, the proposed risk-constrained offering and bidding model turns into a tri-objective optimization problem in which the normal boundary intersection (NBI) procedure is applied for its solution. The numerical results demonstrate that the proposed framework is well capable of simultaneously reaching risk-taker and risk-averse strategies. • Establishing a self-scheduling model for a renewable-storage-thermal power producer. • Modeling the uncertainty of the intraday demand response exchange market price. • Proposing a multi-objective risk-based hybrid stochastic-interval methodology. • Solving the elaborated problem using the normal boundary intersection (NBI) method. • Deriving risk-controlling solutions concerning stochastic and interval uncertainties. [ABSTRACT FROM AUTHOR]