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• A MILP model is introduced to regulate peak demand. • Efficient coordination of DERs and load shifting to improve LF. • Optimal bulk scheduling of equipment used in the EN. • Mutual benefits for customers and the energy company. • The proposed model found the optimal solution to the problem. In recent decades, in urban centers, several services (such as health, food, security, connectivity, etc.) have become more dependent on electricity. With the population increase, the energy requirements of these services can lead the electricity network (EN) to a state of operational stress. In this scenario, distribution companies (DISCOs) need to develop efficient energy management strategies to avoid wasting electricity in its EN. The load factor (LF) of an EN is an indicator related to efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers, together with the operation of distributed energy resources (DERs), such as renewable energy sources (RESs) and capacitor banks (CBs), represents a promising alternative. In order to address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DERs in the EN. Operational constraints related to the performance of EN, RESs, and CBs are considered. Uncertainties in the habitual demand profiles and solar irradiance during the day are represented through simulation algorithms. In order to evaluate the proposed model, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring an efficient EN operation. [ABSTRACT FROM AUTHOR]

The coordination of various energy vectors under the concept of multi-energy system (MES) has introduced new sources of operational flexibility to system managers. In this paper, the behavior of multi-energy players (MEP) who can trade with more than one energy carrier to maximize their profits and mitigate their operational risks has been investigated. The MES is represented based on a multilayer structure, namely the energy market, MEP, the local energy system (LES), and multi-energy demand. In such environment, an MEP aggregates LES and participates in the wholesale electricity market, simultaneously to maximize its profit. The decision-making conflict of the MEP with other energy players for the aggregation of LES and participation in the electricity market is modeled based on a bilevel approach. Numerical results show the behavior of the MEP as a prosumer in the electricity market to produce smoother demand and price profiles. Results reveal a mutual effect of local and wholesale equilibrium prices by increasing the share of the MEP. [ABSTRACT FROM PUBLISHER]

Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn. [ABSTRACT FROM AUTHOR]

In deregulated electricity markets, producers offer their energy to the day-ahead market. As the subsidies for renewable producers are becoming lower and lower, they have to adapt to market prices. This paper models the energy trading in the day-ahead market for wind power producers. Different strategies are proposed for this purpose: 1) several wind farms offering their energy separately to the day-ahead market; 2) the same strategy as in 1) but compensating the imbalance among different wind farms; and 3) a joint offer involving several wind farms through an external agent in order to minimize the imbalances between the offer and the final power generation. The strategies are modeled with stochastic mixed integer linear programming and Conditional Value at Risk is used to consider the risk assessment. The expected profit including risk aversion is maximized for each wind power producer and for the set of wind power producers in the case of a joint offer. A comparison of the different cases is described in detail in a case study and relevant conclusions are provided. [ABSTRACT FROM PUBLISHER]

Thermal generators' emissions constitute an externality on which a regulator might wish to impose constraints. In addition, transmission capacity for sending energy may be naturally restricted by the grid facilities. Both pollution standards and transmission capacity impose constraints, coupling the strategies of the agents in their joint strategy space. To force competitive electricity generators to respect those constraints when individual monitoring is unavailable we envisage the regulator solving a generalized Nash equilibrium problem to establish an equilibrium in which the joint constraints are satisfied. If the regulator appropriately modifies the generators' payoffs then they will play a decoupled game in which they can ignore the information on the joint constraints. For the payoff modification to induce the required behavior, a coupled constraints equilibrium needs to exist and be unique. We borrow from the electrical engineering (EE) literature a three-node network model that has these properties and use it in this paper to discuss and explain the behavior of the agents subjected to the coupled constraints. We find that the imposition of transmission and environmental restrictions may increase the output share of an inefficient generator and decrease consumer surplus. [ABSTRACT FROM AUTHOR]

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: In competitive electricity markets, companies simultaneously offer their productions to obtain the maximum profits on a daily basis. In the long run, the strategies utilized by the electric companies lead to various long-term equilibria that can be analyzed with the appropriate tools. We present a methodology to find plausible long-term Nash equilibria in pool-based electricity markets. The methodology is based on an iterative market Nash equilibrium model in which the companies can decide upon their offer strategies. An exponential smoothing of the bids submitted by the companies is applied to facilitate the convergence of the iterative procedure. In each iteration of the model the companies face residual demand curves that are accurately modeled by Hermite interpolating polynomials. We introduce the concept of meta-game equilibrium strategies to allow companies to have a range of offer strategies where several pure and mixed meta-game Nash equilibria are possible. With our model it is also possible to model uncertainty or to generate price scenarios for financial models that assess the value of a generating unit by real options analysis. The application of the proposed methodology is illustrated with several realistic case studies. [ABSTRACT FROM AUTHOR]

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]

Abstract: This paper presents the complete design of a photovoltaic installation that may be either used for internal electric consumption or for sale using the premium subsidy awarded by the Spanish Government. Electric optimization strategies are detailed in the project, as well as the sizing of the photovoltaic installation and economic and financial issues related to it. The project optimizes the electricity demand, improving reactive power and studying the convenience of hourly discrimination fees in addition to the design of the photovoltaic installation. A specific computer application for the automated calculation of all relevant parameters of the installation—physical, electrical, economical as well as ecological—has been developed to make the process of calculating photovoltaic installations easier and to reduce the design development time. Moreover, the budget of the photovoltaic installation is included, as well as its corresponding financial ratios and payback periods. Finally, the conclusions reached in the technical and economic design of the installation are shown. [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]

Provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA time series analysis; Numeric results; Conclusion.

Presents a study that developed electricity price forecasting tools based on time series analysis. Significance of next-day price forecasting for power consumers and energy service companies; Details on the time series analysis; Results and discussion.

Studying the evolution of the efficiency of the electricity generation sector is a relevant task for policy makers, and requires the undesirable outputs derived from the activity to be considered in the evaluation. In this work, we propose a dynamic slack-based Data Envelopment Analysis (DEA) model that incorporates the assumption of weak disposability between the generation of electricity from fossil sources and the CO2 emissions caused by the sector to measure the technical efficiency of 24 Latin American and Caribbean countries in the period 2000–2016. The results show that, of the total number of countries studied, four are efficient overall, and four groupings of countries in relation to the levels of efficiency achieved are also identified. These results are important given that less-efficient countries can, through learning, increase their efficiency in electricity generation or emulate the future strategies proposed by the most efficient countries. [ABSTRACT FROM AUTHOR]

In electricity markets, bilateral contracts (BC) are used to hedge against price volatility in the spot market. Pricing these contracts requires scheduling from either the buyer or the seller aiming to achieve the highest profit possible. Since this problem includes different players, a Generation Company (GC) and an Electricity Supplier Company (ESC) are considered. The approaches to solve this problem include the Nash Bargaining Solution (NBS) equilibrium and the Raiffa–Kalai–Smorodinsky (RKS) bargaining solution. The innovation of this work is the implementation of an algorithm based on the RKS equilibrium to find a compromise strategy when determining the concessions to be made by the parties. The results are promising and show that the RKS approach can obtain better results compared to the Nash equilibrium method applied to a case study. [ABSTRACT FROM AUTHOR]

A fundamental challenge in power system planning is how to handle the interactions of participants’ behaviors in deregulated markets. This is important due to the high cost involved in their decisions. Proactive or anticipative transmission expansion planning models have been proposed by some authors to jointly model the interactions among deregulated electricity market participants making market-driven investment decisions. Several works have shown that a Transmission Network Planner can increase social welfare by anticipating line expansion planning to generation expansion equilibrium and market outcomes. However, proactive transmission expansion decisions may lead to suboptimal solutions when the generation expansion equilibrium problem have multiple solutions (i.e., leading to higher total costs and lower social welfare). We propose a methodology to study the potential impacts of proactive expansion planning on generation expansion decisions. The resulting formulation is stated as a mathematical program subject to an equilibrium problem with equilibrium constraints (EPEC). To deal with this problem, we also propose an approach to derive tractable EPEC solutions with global optimality guaranteed based on a column-and-row generation algorithm. Our numerical results shows that a proactive investment plan can lead to higher total cost than not investing at all because of the existence of multiple market-driven generation expansion equilibria. We show that the proposed algorithm significantly reduce the time of computation up to two orders of magnitude with respect to existing methodologies. [ABSTRACT FROM AUTHOR]

Energy hubs play an important role in implementing multi-carrier energy systems. More studies are required in both their modeling and operating aspects. In this regard, this paper attempts to develop medium-term management of an energy hub in restructured power systems. A model is presented to manage an energy hub which has electrical energy and natural gas as inputs and electrical and heat energy as outputs. Electricity is procured in various ways, either purchasing it from a pool-based market and bilateral contracts, or producing it from a Combined Heat and Power (CHP) unit, a diesel generator unit and Wind Turbine Generators (WTGs). Pool prices and wind turbine production are subject to uncertainty, which makes energy management a complex puzzle. Heat demand is also procured by a furnace and a CHP unit. Energy hub managers should make decisions whether to purchase electricity from the electricity market and gas from the gas network or to produce electricity using a set of generators to meet the electrical and heat demands in the presence of uncertainties. The energy management objective is to minimize the total cost subject to several technical constraints using stochastic programming. Conditional Value at Risk ( CVaR ), a well-known risk measure, is used to reduce the unfavorable risk of costs. In doing so, the proposed model is illustrated using a sample test case with actual prices, load and wind speed data. The results show that the minimum cost is obtained by the best decisions involving the electricity market and purchasing natural gas for gas facilities. Considering risk also increases the total expected cost and decreases the CVaR . [ABSTRACT FROM AUTHOR]

Multi-carrier systems have become an important research topic in view of electricity and gas systems synergies. We present a new model for the cross-carrier multilateral economic dispatch (MED) problem to account for transmission losses and network constraints in the integrated electricity and gas systems. To associate multilateral trades more effectively with the losses occurring in the network, the loss allocation ratio between suppliers and consumers involved in multilateral trades is introduced in the loss allocation policy. Two sets of reciprocity constraints are presented to ensure the equilibrium between production and consumption, both physically and economically. The price decomposition involved in multilateral trades is developed based on cross-carrier MED with product differentiation (PD). Combined with characteristics of price decomposition, an effective payment to keep less trading information access by the network operator in the decentralized market is presented. Numerical results show that when all losses are allocated to consumers, the MED model without PD performs similarly to a pool-based market model. It is also demonstrated that the inclusion of agents' preferences in the MED model can improve total social welfare. [ABSTRACT FROM AUTHOR]

Highlights: [•] The new approach to allocate transmission cost. [•] The approach is based on LRMC method and the min–max optimization technique. [•] The approach reduces the dispersion of the tariffs maintaining the locational signal. [•] An iterative method to set tariffs based on a linear programming optimization model. [Copyright &y& Elsevier]