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In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

The wholesale electricity and capacity market constitute the backbone of the Russian power industry. It is in this market that large suppliers and buyers operate, and its entire turnover is consequently transmitted to the retail market. Our paper presents a theoretical overview of the main tools for forming the cost of electricity and capacity in the wholesale market in Russia (depending on the regional affiliation), the type of end users, and the degree of state participation. We consider the specifics of the formation of the cost of electricity and capacity in the price and non-price wholesale markets of Russia, which differ in territorial, climatic, and economic characteristics, as well as the established structure of generation. In the empirical part of the paper, we carry out a structural analysis of the volumes of trade in electricity and capacity in the price and non-price zones of the market. Furthermore, we explain the reasons for the current dynamics of prices in the wholesale market. Using the obtained results, we calculate the maximum annual effect of the solar power plant operation in various zones of the Russian wholesale market, as well as in the retail market. In addition, we estimate the economic incentive for the transition of the functioning of power facilities from the wholesale to the retail market. Our results can be of considerable practical importance and might be used for improving the strategy for the development of the electric power industry at the regional level both in Russia and in the other countries.

Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand.

The penetration of renewable energies in the energy market has increased significantly over the last two decades due to environmental concerns and clean energy requirements. The principal advantage of renewable energy resources (RESs) over non-RESs is that it has no direct carbonisation impact on the environment and that it has none of the global warming effects which are caused by carbon emissions. Furthermore, the liberalisation of the energy market has led to the realisation of the virtual power plant (VPP) concept. A VPP is a unified platform for distributed energy resources that integrates the capacities of various renewable energies together for the purpose of improving power generation and management as well as catering for the buying and selling of energy in wholesale energy markets. This review study presents a comprehensive review of existing approaches to planning, operation and scheduling of the VPP system. The methodologies that were adopted, their advantages and disadvantages are assessed in detail in order to benefit new entrants in the power system and provide them with comprehensive knowledge, techniques and understanding of the VPP concept.

Distributed market structures for local, transactive energy trading can be modeled with ecological systems, such as mycorrhizal networks, which have evolved to facilitate interplant carbon exchange in forest ecosystems. However, the complexity of these ecological systems can make it challenging to understand the effect that adopting these models could have on distributed energy systems and the magnitude of associated performance parameters. We therefore simplified and implemented a previously developed blueprint for mycorrhizal energy market models to isolate the effect of the mycorrhizal intervention in allowing buildings to redistribute portions of energy assets on competing local, decentralized marketplaces. Results indicate that the applied mycorrhizal intervention only minimally affects market and building performance indicators-increasing market self-consumption, decreasing market self-sufficiency, and decreasing building weekly savings across all seasonal (winter, fall, summer) and typological (residential, mixed-use) cases when compared to a fixed, retail feed-in-tariff market structure. The work concludes with a discussion of opportunities for further expansion of the proposed mycorrhizal market framework through reinforcement learning as well as limitations and policy recommendations considering emerging aggregated distributed energy resource (DER) access to wholesale energy markets.

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

The European Union (EU) has the most advanced, mature, and liberal energy markets that gave rise to the most dramatic drop in wholesale energy prices, whose fallen, however, has not been translated into a reduction in retail energy prices. Instead, energy prices in Europe rose above inflation year-in-year-out, and are considerably higher compared with major economic partners. This paper highlights the key limitations in the EU market designs and network access toward renewable integration, and the wide range of reforms that the EU is currently undertaken across the Member States to achieve two goals: to make the market fit for renewable, and to set a practical example of how a competitive economy can be built on a sustainable and affordable energy system. This paper concludes with key recommendations to developing nations, particularly in addressing heavy renewable curtailment.

One of the most important economic strategic sectors in any economy is the electricity market. Its main feature is its oligopolistic character favoured by the returns to scale which act as an entry barrier. As a result, the energy generators can use their power market in order to increase their benefits through the daily offered price and quantity of energy for each of their power plants. This paper presents a methodology for estimating the daily offered price of the most important power stations in Colombia (hydraulic and ther- mal) by applying artificial intelligence techniques: Fuzzy Logic and Neural Networks. Such techniques are found to be partially useful particularly for price tendencies. It also compares the results with autoregressive models that turned out inappropriate for the case of study.

Uno de los sectores económicos estratégicos más importantes en cualquier economía es el Mercado de Energía Mayorista, cuya característica fundamental es que se trata de un mercado oligopolístico, provocado por la barrera de entrada que supone tener economías de escala. De esta manera, los agentes pueden presentar comportamientos estratégicos que contribuyen a la maximización de sus utilidades, los cuales se ven reflejados en la oferta diaria del precio y de la cantidad de energía por hora en cada una de sus centrales de generación. En este trabajo se presenta una metodología para la estimación de los precios diarios a los que ofertan la energía que producen los principales recursos hídricos y térmicos en Colombia. Se emplean dos herramientas de Inteligencia Artificial: la Lógica Difusa y las Redes Neuronales. Dichas técnicas resultan ser parcialmente efectivas para seguir las tendencias de dichos precios. También se comparan los resultados con los de modelos autorregresivos, que resultan ser inapropiados para el caso de estudio. || One of the most important economic strategic sectors in any economy is the electricity market. Its main feature is its oligopolistic character favoured by the returns to scale which act as an entry barrier. As a result, the energy generators can use their power market in order to increase their benefits through the daily offered price and quantity of energy for each of their power plants. This paper presents a methodology for estimating the daily offered price of the most important power stations in Colombia (hydraulic and thermal) by applying artificial intelligence techniques: Fuzzy Logic and Neural Networks. Such techniques are found to be partially useful particularly for price tendencies. It also compares the results with autoregressive models that turned out inappropriate for the case of study.

Economic evaluations of alternative electric generating technologies typically rely on comparisons between their expected “levelized cost” per MWh supplied. I demonstrate that this metric is inappropriate for comparing intermittent generating technologies like wind and solar with dispatchable generating technologies like nuclear, gas combined cycle, and coal. It overvalues intermittent generating technologies compared to dispatchable base load generating technologies. It also likely overvalues wind generating technologies compared to solar generating technologies. Integrating differences in production profiles, the associated variations in wholesale market prices of electricity, and life-cycle costs associated with different generating technologies is necessary to provide meaningful comparisons between them.