Liberalized Electricity Market Research Articles

Optimal Energy Storage Allocation Strategy by Coordinating Electric Vehicles Participating in Auxiliary Service Market

The further liberalization of China's electricity market encourages demand-side entities to participate in electricity market transactions. Electric vehicles (EVs) are developing rapidly and have high regulating potential, and are the main force for demand-side participation in the auxiliary service market. Aiming at the problems of dispatching accuracy and economy in EV participation in auxiliary service market, this paper analyzes the bidding strategy and dispatching scheme of EV-storage participation in auxiliary service market, and proposes EV-storage optimal allocation strategy with the goal of economic optimum. In the process of optimal allocation, based on the market rules of third-party subject participation in auxiliary services, the bidding strategy of EV-storage coordinated EV participation in auxiliary services market considering daily load scale changes is designed, while the conditional value at risk (CVaR) method is used to determine the short-term coordinated energy storage capacity and efficiency storage capacity considering the uncertainty of EV response situation; then, based on the annual EV load scale change, the benefit calculation function is constructed by considering various factors such as auxiliary service market revenue, spread revenue, investment cost and market opportunity cost of EV-storage participation in the auxiliary market. Finally, taking EV aggregation participation in the valley-filling ancillary service market as an example, it is verified that the strategy proposed in this paper can effectively improve the responsiveness of EV participation in the ancillary service market and increase the revenue of electric vehicle aggregator (EVA).

The Problems of Electricity Market Liberalization in Uzbekistan

The Problems of Electricity Market Liberalization in Uzbekistan

A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting

Electricity price forecasting plays a crucial role in a liberalised electricity market. Generally speaking, long-term electricity price is widely utilised for investment profitability analysis, grid or transmission expansion planning, while medium-term forecasting is important to markets that involve medium-term contracts. Typical applications of medium-term forecasting are risk management, balance sheet calculation, derivative pricing, and bilateral contracting. Short-term electricity price forecasting is essential for market providers to adjust the schedule of production, i.e., balancing consumers' demands and electricity generation. Results from short-term forecasting are utilised by market players to decide the timing of purchasing or selling to maximise profits. Among existing forecasting approaches, neural networks are regarded as the state of art method due to their capability of modelling high non-linearity and complex patterns inside time series data. However, deep neural networks are not studied comprehensively in this field, which represents a good motivation to fill this research gap. In this article, a deep neural network-based hybrid approach is proposed for short-term electricity price forecasting. To be more specific, categorical boosting (Catboost) algorithm is used for feature selection and a bidirectional long short-term memory neural network (BDLSTM) will serve as the main forecasting engine in the proposed method. To evaluate the effectiveness of the proposed approach, 2018 hourly electricity price data from the Nord Pool market are invoked as a case study. Moreover, the performance of the proposed approach is compared with those of multi-layer perception (MLP) neural network, support vector regression (SVR), ensemble tree, ARIMA as well as two recent deep learning-based models, gated recurrent units (GRU) and LSTM models. A real-world dataset of Nord Pool market is used in this study to validate the proposed approach. Mean percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) are used to measure the model performance. Experiment results show that the proposed model achieves lower forecasting errors than other models considered in this study although the proposed model is more time consuming in terms of training and forecasting.

Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case

Since the electricity market liberalisation of the mid-1990s, forecasting energy demand and prices in competitive markets has become of primary importance for energy suppliers, market regulators and policy makers. In this paper, we propose a non-parametric model to obtain point and interval predictions of price and demand. It does not require any parametric assumption on the distribution of the error term or on the functional relationships linking the response variable to covariates. The assumed location–scale model provides a non-parametric estimation of the conditional mean and of the conditional variance by means of a Generalised Additive Model. Interval forecasts, at any given confidence level, are then obtained using a further non-parametric estimation of the innovation’s quantile. Since both the conditional mean and the conditional variance of the response variable are non-linear functions of covariates depending on calendar factors, renewable energy productions and other market variables, the resulting model is very flexible. It easily adapts to market conditions as well as to the non-linear characteristics of demand, supply and prices. An application to hourly data for the Italian electricity market, over the period 2015–2019 period, shows the one-day-ahead forecasting performance of the model for zonal electricity prices and level of demand.

Pumped Hydroelectric Energy Storage as a Facilitator of Renewable Energy in Liberalized Electricity Market

Besides many benefits deriving from the energy transition process, it is not uncommon for modern power systems to be faced with difficulties in their operation. The issues are dominantly related to the non-dispatchable nature of renewable energy sources (RES) and the mismatching between electricity generation and load demand. As a consequence of a constant peak load growth, this problem is particularly pronounced during the daily peak hours. Therefore, it is of great importance to conduct all necessary activities within the system in order to preserve the system stability and continuity of operation. Energy storage systems have been recognized as a major facilitator of renewable energy, by providing additional operational flexibility. Since pumped hydroelectric energy storage (PHES) accounts for almost 97% of the world’s storage capacity, in this paper, we have investigated the benefits of using pumped-storage hydropower in modern power systems characterized by high penetration of RES and the liberalized electricity market. A novel operation algorithm has been developed which finds the balance between providing additional flexibility by alleviating the peak load and obtaining financial revenue to justify the high investment costs associated with PHES. The algorithm has been tested for the daily and monthly operation of the Tonstad PHES in the dynamic environment of the Norwegian power system.

Geostrategic Renewable Energy Transition in Turkey: Organizational Strategies Towards an Energy Autonomous Future

The geographical location of Turkey in the Asia Minor places the country in a delicate geostrategic position determined by its history, ideological structure, politics and energy economy. The Turkish government has defined its main energy strategies with the goal of reaching 30% renewables by 2023. Key strategies declared are the prioritization of energy supply security, the consideration of environmental concerns, and an increase in efficiency and productivity through the establishment of transparent and competitive market conditions through reform and liberalization. This article analyses the renewable energy (RE) transition of Turkey from a fully centralised energy management model towards a system of partially centralization through the unbundling of utility companies. Analysis will utilize Michael Mann’s theory on the four sources of social power as an alternative organizational means of social control and the interrelations of ideological, economic, political and military power. The recent history of Turkey’s RE transition and government plans for sector development will be investigated from a socio-spatial and organizational perspective. Furthermore, the way in which these socio-spatial relations have been shaping electricity market liberalization and the preparedness of the state to share its power with non-state actors is discussed. The potential of a centralised RE management model to inspire ‘decentralised’ RE management in other geographies is considered. In conclusion, key factors in the organisation of the (de)centralised electricity transitions are found to be dependent on history, geography, and overlapping relations of social power.

Lithuanian electricity market price forecasting model based on univariate time series analysis

Electricity price changes can significantly affect expenses in energy intensive industries, adjust profits or losses for electricity retailers and cause problems for country’s national energy strategy implementation. Forecasting models based on statistical methods and previous variable values help to predict future values and adjust strategy according to the forecast. This paper concentrates on the Lithuanian electricity market and presents the widely used ARIMA forecasting models based on the univariate time series analysis. The Lithuanian electricity market is selected due to a lack of statistical researches based on electricity market prices in Lithuania, as well as significant future electricity market liberalization projects. Electricity price data for analysis are taken from the Nord Pool electricity market operator website. The Nord Pool represents the Northern Europe electricity market operator where Lithuania and other 14 European countries trade electricity on a daily basis. To provide a long-term electricity price outlook average monthly data from July 2012 to December 2019 are selected for analysis. Before building the ARIMA model data are tested with various statistical tests to guarantee that time series are stationary, there is no autocorrelation or structural breaks. Once the data validity is confirmed, the time series is divided into train and test sets. The train data set is used to create a fitting ARIMA model, while the test set is used to define forecasting accuracy. Created forecasts of models are compared between each other using common comparison statistics, and the most accurate models are defined. Finally, the selected model is trained on a full dataset and the electricity price forecast for the year 2020 is constructed. The created AR (1) model had the smallest error value compared to the test dataset, while the SARIMA (1,1,1) model had the best approximation statistics. By combining both models the weighted SARIMA (1,1,1) model is constructed with the features of low forecasting error and precise actual time series approximation. The final model forecast for the year 2020 shows the monthly average electricity price decrease at the beginning of the year, a significant increase at the second half of the year and a price drop at the end of the year. Forecasting results can help companies to plan their electricity production and maintenance periods to maximize income from sold energy and minimize potential losses due to planned shutdown.

Transmission Embedded Cost Allocation using Proportional Nucleolus Method in a Liberalized Electricity Market

Transmission Embedded Cost Allocation using Proportional Nucleolus Method in a Liberalized Electricity Market

Distributed Generation: A Critical Review of Technologies, Grid Integration Issues, Growth Drivers and Potential Benefits

Owing to liberalization of electricity market, technology evolution, energy security, environmental issues and growing concerns of energy cost, the penetration of distributed energy units in distribution network is increasingly observed worldwide. Penetratingrembedded generation, or distributedrgeneration (DG), in powerrdistribution grid requires asnumber of issues to be considered, such as definition of DG, rating of DG, the best DG technology etc. This paper presents an extensive critical review of various dimensions of distributed generation (DG) including definitions, generation technologies and their status, impact on distribution network performance etc. The study also presents comparative study between the various technologies in terms of most important technological characteristics of each DG technology. The policy makers, utility regulators and DG planning engineers can use this critical review, withoutigoing through complicatedicomputations, as guidelines to makeipolicies, standards andidecisions in DG penetration andirelated issues.

The Value of Solar Photovoltaics in an Isolated Competitive Electricity Market: a Case Study of Singapore

With a view of achieving its obligations under the global climate change regime, and low technical potential for other renewable energy technologies, solar photovoltaics (PV) is the main focus for Singapore to generate low carbon electricity. However, a high penetration of PV in Singapore’s electricity market would result in a number of costs and benefits that need to be quantified in order to ensure prudent government policies for “supporting” the widespread adoption of PV in Singapore. The quantification of these costs and benefits would allow policymakers to realize the true “value” of solar PV for Singapore and would address the limitations of relying on the levelised cost of electricity metric. This paper provides an assessment of the societal value that PV would bring to Singapore, essentially an isolated power system with a liberalized electricity market. For the purpose of this study, the determination of the value of solar involves quantifying the savings due to the merit order effect, costs due to additional frequency control services, and net avoidance of CO2 emissions.

How Choice Complexity in Liberalized Markets Hurts the Demand for Green Electricity

Many countries have liberalized their residential electricity markets or are considering to do so. Liberalization provides consumers with more freedom of choice but also leads to higher choice complexity as consumers face a much larger number of different electricity contracts to choose from. We hypothesize that consumers react to this increased choice complexity in liberalized markets by applying simplified decision strategies that allow them to reduce cognitive effort. In particular, we predict that with increasing size of choice sets, consumers focus more on simple price attributes of electricity contracts and less on the relatively complex environmental attributes, leading to a decrease in the demand for green electricity. In two online experiments conducted in a representative (n=610) and a student sample (n=1,212) in Switzerland, we find that indeed when faced with a larger choice set participants focus more on prices and choose cheaper electricity contracts containing less renewable and more conventional energy than when faced with a smaller choice set. In addition, we also find evidence that a tax on conventional energy is a more effective policy instrument for shifting demand towards renewables than behavioral instruments in the form of social norm interventions. Our results suggest that a liberalization of the household electricity market has to be carefully managed such that consumers are not overwhelmed and do not shift their demand to cheaper but less environmentally-friendly energy sources.

Predicting collusive patterns in a liberalized electricity market with mandatory auctions of forward contracts

In this paper, the main objective is to analyze the effect of the introduction of regulated long-term tariffs in liberalized electricity generation markets, which are supposed to guarantee reasonable and predictable prices to consumers. Several authors have argued that the introduction of this kind of regulation increases competition and leads to offer prices closer to marginal costs.Spanish electricity market brings a unique opportunity to analyze and use mandated auctions as a natural experiment (fixed-price contracts that were implemented between 2007 and 2013). The main contribution of this study is to develop and to validate the hypothesis of the existence of strong incentives to increase prices in daily electricity markets when fixed long-term tariffs are applied. Using triple difference-in-differences, we find that prices increased 15 percent 70 days before the mandated auctions (compared to a control group: Nord Pool market) and that effect disappeared once the auction was no longer carried out.The theoretical and the empirical findings support our policy implications: The uncompetitive outcome seems to be driven by a highly concentrated market, a high elasticity to react to competitive regulations, a context of high volatility and the particularities of the auction design.

When competition plays clean: How electricity market liberalization facilitated state-level climate policies in the United States

Why do some governments adopt policies to mitigate climate change while others do not? In this study, I illustrate the importance of industrial organization in shaping prospects for climate mitigation policy. Using a difference-in-differences analysis, I show that U.S. states which adopt electricity market liberalization laws are subsequently fifteen to forty-three percentage points more likely to adopt a renewable portfolio standard and fifteen to thirty percentage points more likely to adopt a cap-and-trade program. I argue that the forced reduction in market share of incumbent utilities associated with market liberalization laws undermined the political dominance of legacy producers and facilitated the growth of independent power producers (IPPs). Following liberalization, I show how these firms disproportionately benefited from the development of renewable energy, and how their growth in market share corresponded with a rise in political activity, thus offering a countervailing industrial interest-group influence to legacy producers. These findings demonstrate the importance of industrial organization in shaping long-run prospects for climate mitigation. More generally, this study sheds light on how government interventions can shape industrial interest-group dynamics.

An integrated approach to electricity sector reforms in the resource rich economies of the MENA

This paper argues that MENA resource-rich economies must go beyond simply replicating the “standard model” of electricity market liberalisation. They need to not only adapt the standard model to their unique contexts but also integrate it with other elements of their energy systems through harnessing complementarities between existing policies. The integrated model therefore includes additional modules: rationalising end user prices, improving energy efficiency, integrating renewables, and collapsing the “silos” between different energy vectors. The success of the extended reform model, however, is crucially contingent upon taking into account three particular factors in its implementation. First, reforming energy prices requires a better understanding of the underpinning logic of subsidies beyond popular justifications around the “social contract” or “political compact”. Second, energy efficiency is a challenging initiative in the MENA region, which the correction of price signals can only partially resolve, due to other factors that influence it, such as path dependency, market failures and consumer behaviour. And third, incentivising investments in renewable energy requires careful design of the balance of roles between the market and government, as renewable support schemes may lead to an increased role for centralised coordination, thereby contradicting the originally intended objectives of electricity market reform.

The Value of Solar Photovoltaics in an Isolated Competitive Electricity Market: a Case Study of Singapore

With a view of achieving its obligations under the global climate change regime, and low technical potential for other renewable energy technologies, solar photovoltaics (PV) is the main focus for Singapore to generate low carbon electricity. However, a high penetration of PV in Singapore’s electricity market would result in a number of costs and benefits that need to be quantified in order to ensure prudent government policies for “supporting” the widespread adoption of PV in Singapore. The quantification of these costs and benefits would allow policymakers to realize the true “value” of solar PV for Singapore and would address the limitations of relying on the levelised cost of electricity metric. This paper provides an assessment of the societal value that PV would bring to Singapore, essentially an isolated power system with a liberalized electricity market. For the purpose of this study, the determination of the value of solar involves quantifying the savings due to the merit order effect, costs due to additional frequency control services, and net avoidance of CO2 emissions.

Coordination of electricity transmission and generation investments

For over twenty years, electricity market liberalization has advanced short-term market efficiency in wholesale markets, but little progress has been made on coordinated transmission and generation investments. With fast penetration of renewables, widespread of distributed energy resources and emergence of smart-grid and energy storage technologies, the stake is growing significantly higher for the long-term efficiency of liberalized wholesale markets with an aging transmission infrastructure.This paper presents a methodology based on a general framework of two-stage stochastic optimization model for analyzing interactions between transmission and generation investments. The methodology features economic effects of prices, incentives and welfare impacts under alternative coordination approaches and cost recovery mechanisms. Application to scenarios under efficient coordination, merchant transmission, and sequential coordination are illustrated using a simple radial transmission network. Interestingly, a Boiteux-Ramsey cost sharing rule produces energy prices and social welfare impacts that appear essentially indistinguishable from those obtained under efficient coordination.

Managing the liberalization of Italy's retail electricity market: A policy proposal☆

Italy will phase out electricity retail price regulation by July 1st, 2020. Until then, residential customers and small businesses who do not choose their supplier are served under a regulated tariff named “maggior tutela” (greater protection), supplied by the local distributor at a price set by the regulator. We review the literature on electricity retail competition – with particular regard to its expected effects on prices, innovation, and customer engagement – and the conditions under which competition is expected to deliver benefits. We perform a Structure-Conduct-Performance analysis of Italy's retail electricity market for residential customers, finding two issues potentially problematic: excessive market concentration and low customer engagement. We propose a phase-out mechanism that relies on graduality, asymmetric regulation, and a mandatory, opt-out collective switching exercise. The mechanism aims to rapidly reduce market concentration by leveraging behavioral incentives to customers to switch to the cheapest supplier.

Distribution Network Planning Methodology With Ed Mostar Case Study

Abstract Power systems around the world have undergone a number of important organizational, structural and technological changes over the past few decades; they are a direct consequence of the electricity market liberalization and transition from conventional energy conversion technologies towards renewable resources. These changes represent many advantages as well as challenges for the Distribution System Operator (DSO). The aim of this paper is to review the most important principles, objectives and technical criteria used in planning the development of the electricity distribution network. Presented principles can be used as basic guidelines when developing short-term and long-term plans for the construction and reconstruction of power distribution facilities. This paper also presents a methodological approach to the planning and ranking of proposed electricity facilities with an example from practice that is based on the real planning problem in ED Mostar. The basic conclusion of the paper is that the identification of objectives, criteria and the application of an appropriate and unique methodology is of the utmost importance for formulating the framework of the planning process.

Emissions trading in regulated electricity markets

ABSTRACT In theory, by establishing a price for greenhouse gas (GHG) emissions, an Emissions Trading System (ETS) promises to encourage a shift away from carbon-intensive electricity supply. It is generally assumed that this decarbonization effect requires the liberalization of electricity markets. However, amongst the more than 40 jurisdictions throughout the world that are now implementing, planning or considering an ETS to decarbonize electricity supply, few have adopted the electricity market liberalization textbook model. Economic agents are constrained in how they can respond to an ETS through various forms of electricity market regulation but, given the political sensitivity of electricity market reform and the urgency to decarbonize the electricity sector, it is unrealistic to advocate liberalization as a prerequisite to the introduction of an ETS. Building from regulatory theory on the design of electricity and environmental markets, we develop a conceptual framework to understand the interaction between ETS and electricity market regulation under different regulatory settings. Our analysis challenges the general assumption that the introduction of an ETS depends on electricity market liberalization. We argue that the ETS can be adjusted to the electricity market reality of many jurisdictions where governments limit price discovery, restrict price pass-through or place constraints on investment decisions, and discuss regulatory alternatives to address the efficiency losses that may emerge from specific forms of power sector regulation. Key policy insights Although an ETS will be most effective when it is implemented within a liberalized and competitive electricity market, it can also be introduced in a regulated electricity market environment, albeit with some efficiency losses. Where an ETS is implemented under power sector regulation, it is important to understand where barriers to mitigation exist, how much mitigation potential will be lost, and which other policies can target these areas of the economy. A proportion of the efficiency loss might be restored through innovative emissions trading policy design that overcomes the barriers to mitigation put in place through power sector regulation. This is a topic that deserves more academic research.

Impacts of hybrid time-varying tariffs on residential electricity demand: The case of Zhejiang Province

With the further liberalization of China's electricity market, electricity price becomes more important in the power supply-demand balance. We investigate the impacts of a hybrid time-varying tariff policy on residential electricity demand. In our estimation, we maximize the residential users' utility, taking average price responsiveness as their perceived price. Four indices related to power usage and expenses are designed to measure the response effects of time-varying tariff policy using a case study of Zhejiang province.