Competitive Electricity Research Articles

Effects of self-generation in imperfectly competitive electricity markets: The case of Spain

Domestic rooftop photovoltaic (PV) energy can reduce net electricity demand, and therefore reduce energy prices through a merit-order effect. This reduces profits of all incumbents in the electricity markets. In addition, in imperfectly competitive markets, PV self-generation reduces prices through a reduction in market power. The first effect may warrant additional policy interventions to maintain cost recovery, but the second is much more desirable, as it simultaneously helps increase sustainability and competition. However, unlike a simple reduction in market prices, the competition effect affects all incumbents differently. Since resistance from incumbents can be a significant barrier to energy policy change, it is important to understand the distribution of effects. This paper does so for the Spanish market. A Nash-Cournot model and a simplified representation of the Spanish electricity market is used to determine the merit-order and competition effects of an increase in solar self-generation. We conclude that both are important, and that their analysis is essential to inform the social debate around PV policy.

Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network

In this paper, a Hopfield Lagrange network (HLN) method is applied to solve the optimal load dispatch (OLD) problem under the concern of the competitive electric market. The duty of the HLN is to determine optimal active power output of thermal generating units in the aim of maximizing the benefit of electricity generation from all available units. In addition, the performance of the HLN is also tested by using five different functions consisting of the logistic, hyperbolic tangent, Gompertz, error, and Gudermanian functions for updating outputs of continuous neurons. The five functions are tested on two systems with three units and 10 units considering two revenue models in which the first model considers payment for power delivered and the second model concerns payment for reserve allocated. In order to evaluate the real effectiveness and robustness of the HLN, comparisons with other methods such as particle swarm optimization (PSO), the cuckoo search algorithm (CSA) and differential evolution (DE) are also implemented on the same systems. High benefits and fast execution time from the HLN lead to a conclusion that the HLN should be applied for solving the OLD problem in a competitive electric market. Among the five applied functions, error function is considered to be the most effective one because it can support the HLN to find the highest benefit and reach the fastest convergence with the smallest number of iterations. Thus, it is suggested that error function should be used for updating outputs for continuous neurons of the HLN.

An investigation on apportion of mathematical loss in transmission loss/cost allocation approach

<p>Cost allocation of highly non-linear transmission loss is complex and essential in competitive electricity market. In most of the existing transmission loss/cost allocation approaches, real power loss depends on selection of slack bus and hence the cost of transmission losses which are allocated to the generators and the loads also varies. In this paper, a complete analysis on the impact of slack bus selection on transmission loss allocation with and without mathematical loss is made. One of the existing approaches, proportional generation and proportional load (PGPL) method is taken to illustrate the impact. Mathematical loss is the loss without generation and load in the network and can be obtained from power flow solution by taking generation and load as zero. The cost incurred for this mathematical loss is allocated to the transmission lines while the cost of transmission loss due to bilateral contracts is allocated among the sources and the consumers. These loss/cost allocations with and without considering mathematical loss is shown using an IEEE 30 bus, 57 bus, 75 bus and 118 bus systems. The simulation results are obtained using MATLAB R2014a.<em></em></p>

Merchant Storage Investment in a Restructured Electricity Industry

Restructuring and liberalisation of the electricity industry creates opportunities for investment in energy storage, which could be undertaken by a profit-maximising merchant storage operator. Because such a firm is concerned solely with maximising its own profit, the resulting storage-investment decision may be socially suboptimal (or detrimental). This paper develops a bi-level model of an imperfectly competitive electricity market. The modelling framework assumes electricity-generation and storage-operations decisions at the lower level and storage investment at the upper level. Our analytical results demonstrate that a relatively high (low) amount of market power in the generation sector leads to low (high) storage-capacity investment by the profit-maximising storage operator relative to a welfare maximiser. This can result in net social welfare losses with a profit-maximising storage operator compared to a no-storage case. Moreover, there are guaranteed to be net social welfare losses with a profit-maximising storage operator if the generation sector is sufficiently competitive. Using a charge on generation ramping between off- and on-peak periods, we induce the profit-maximising storage operator to invest in the same level of storage capacity as the welfare-maximising firm. Such a ramping charge can increase social welfare above the levels that are attained with a welfare-maximising storage operator.

Game Theory Based Profit Maximization Model for Microgrid Aggregators With Presence of EDRP Using Information Gap Decision Theory

In the competitive electricity markets, each microgrid (MG) aggregator strives to maximize its own profit, whereas their coordination is a serious challenge for distribution system operator (DSO). In addition, indefiniteness of market clearing price and pungent alterations of renewable power generation are driving this issue toward a more complicated problem. To address this problem, we proposed an unprecedented game theoretic model by means of Stackelberg strategy to peruse the competition among MGs aggregators and DSO in order to attain the equilibrium point that can maximize the profit of all players who participate in retail market. Our design objective is to distinguish the optimal power exchanges between the MGs and market simultaneously considering emergency demand response program in order to hedge the risk of participation in retail market. Furthermore, information gap decision theory has been fulfilled to cope with drastic uncertainty of the problem. Finally, Benders decomposition (BD) associated with mathematical program with equilibrium constraints and strong duality theorem are accomplished to make the proposed model computationally tractable and subsequently replaced by its Karush–Kuhn–Tucker optimality conditions, which are in turn linearized. The executed detailed and extensive results indicate the sufficiency and competency of the proposed model.

Agent-based dynamic simulation of an electricity market with multilateral bidding

The electricity market is a complex system in which participants interact and compete with each other, which makes description of them with mathematical models difficult. To solve these difficulties, computer simulation has become one of the main methods for studying electricity market problems. How to establish a reasonable electricity market has always been a major research issue in the electric power industry, for which a key point is the bidding mechanism. Agent-based modeling and a simulation (ABMS) method are used in this paper to study the imperfect competitive electricity market. An agent-based simulation method of multilateral bargaining game theory in the dynamics of the power bidding market is presented, and a multi-agent power market bidding dynamics simulation model based on game theory is established. The dynamic bidding game behavior among the government, power grid companies, power plant companies, and consumer parties is simulated in the market, and the simulation method is realized by Anylogic software. Finally, an agent-based four-party competitive dynamic game simulation in the electricity market is implemented, which provides a theoretical reference for further understanding resource optimization problems in the electricity market.

Excessive electricity intensity of Vietnam: Evidence from a comparative study of Asia-Pacific countries

As electricity consumption in Vietnam has continued to increase much faster than has GDP, electricity intensity (EI) in the country has risen to levels far exceeding those of other Asia-Pacific economies (APEs). By analyzing evidence from a comparative study of other APEs through using the World Bank data, this study proves that EI in Vietnam is excessive and that its escalation over the last few decades cannot be justified as being due to supporting the country's policy of high economic growth. Factor analysis of the economic and electricity indicators for 22 APEs was used to track the shortcomings of the economic structure leading to the EI escalation in Vietnam. Electricity tariff, service share of GDP, and level of institution were identified as determinants of EI across the region. Given the weak performance regarding these indicators, Vietnam has highest EI among APEs followed by China and Mongolia. To reduce EI, Vietnam should consider diversifying away from the electricity-intensive industry sector toward economic activities such as service and information technology. The economic reform should be accelerated to complete the competitive electricity market and reduce the inefficiency of electricity usage through poorly managed state-owned enterprises and inefficient public investment projects.

An Economic Analysis of Load Leveling with Battery Energy Storage Systems (BESS) in an Electricity Market Environment: The Korean Case

The capacity of battery energy storage systems (BESS) is expected to increase for power system applications. However, as the cost of BESS is high, economic feasibility must be considered when using BESS in grid applications. Load leveling with BESS is one such application for which the economic implications have been analyzed in the literature. However, these studies do not sufficiently consider the fact that the leveled loads will lead to a change in electricity prices, thereby modifying charging/discharging operations of BESS. Additionally, in a competitive electricity market, electricity prices are not determined by the generator cost functions. Market participants’ strategic decisions also affect prices. Therefore, we conducted an economic analysis of load leveling with BESS in an electricity market from the perspective of a utility company and/or a government agency. In our analysis of the Korean market, we examine whether the leveled loads necessarily lead to economic benefits. Load leveling performance and the associated economic benefit are quantitatively analyzed for varying sizes of BESS. Further, the policy implications related to using BESS are derived from the analysis results.

Production efficiency of nodal and zonal pricing in imperfectly competitive electricity markets

Electricity markets employ different congestion management methods to handle the limited transmission capacity of the power system. This paper compares production efficiency and other aspects of nodal and zonal pricing. We consider two types of zonal pricing: zonal pricing with Available Transmission Capacity (ATC) and zonal pricing with Flow-Based Market Coupling (FBMC). We develop a mathematical model to study the imperfect competition under zonal pricing with FBMC. Zonal pricing with FBMC is employed in two stages, a day-ahead market stage and a re-dispatch stage. We show that the optimality conditions and market clearing conditions can be reformulated as a mixed integer linear program (MILP), which is straightforward to implement. Zonal pricing with ATC and nodal pricing is used as our benchmarks. The imperfect competition under zonal pricing with ATC and nodal pricing are also formulated as MILP models. All MILP models are demonstrated on 6-node and the modified IEEE 24-node systems. Our numerical results show that the zonal pricing with ATC results in large production inefficiencies due to the inc-dec game. Improving the representation of the transmission network as in the zonal pricing with FBMC mitigates the inc-dec game.

Multiperiod Planning of Distribution Networks Under Competitive Electricity Market With Penetration of Several Microgrids Part II: Case Study and Numerical Analysis

This paper aims at investigating the effectiveness of the proposed model in Part I. For this, an IEEE 33-bus distribution system is selected, and the proposed algorithm is performed on it. Simulation results obtained from this system show that placing microgrids (MGs) in the distribution network has benefits for all participants; because the proposed model, in addition to considering the objectives of distribution system operator (DSO), finds the potential places to form MGs such that the obtained benefits of MGs are also provided for investments on establishing MGs at these places. The obtained results of this model are applicable for both DSO and MG investors.

Modeling of bidding strategies in a competitive electricity market: A hybrid approach

AbstractIn the paper, an innovative hybrid algorithm is proposed to solve bidding strategy problem in electricity market. The proposed hybrid technique is the combination of the recurrent neural network (RNN), support vector machine (SVM), and the lightning search algorithm (LSA). The main contribution of this paper is to maximize the profits of suppliers and minimize the customer payments; it is very important to create reasonable matching rule for the electricity market. LSA optimizes bidding coefficients of both the suppliers and consumers with the consideration of available power generation limit, power demand, market‐clearing price (MCP), and constraints. RNN is utilized for demand prediction at every hour, and SVM ensures the MCP based on the demand. The proposed bidding strategy ensures the maximum profit of the suppliers and consumers because of the aggregated demand model. The proposed method is tested on The Institute of Electrical and Electronics Engineers (IEEE) 30‐bus system and 75‐bus Indian practical system and compared with the existing techniques. The comparison results demonstrate the superiority of the proposed technique and confirm its potential to solve the problem. Also, the proposed model and the strategies are implemented in the MATLAB, and the performance will be studied under various demand environments.

Optimal integration of recompression supercritical CO2 Brayton cycle with main compression intercooling in solar power tower system based on exergoeconomic approach

Despite the emerging interest in applying the supercritical CO2 (S-CO2) Brayton cycle to solar power towers (SPTs), its unique characteristics necessitates a specific thermoeconomic consideration in the integration of this cycle in SPT plants to obtain a competitive electricity generation cost. In this work, the exergoeconomic approach is utilized to address the optimal integration of the recompression S-CO2 Brayton cycle with main compression intercooling in the SPT plant. Firstly, exergoeconomic optimization using a genetic algorithm is performed on six crucial variables of S-CO2 Brayton cycle to minimize the total unit exergy cost of the SPT system (cp,tot). The results are then compared with those obtained by thermodynamic optimization aiming at maximal SPT energetic efficiency. Secondly, a sensitivity analysis model is established, the effects of the cost and design conditions of solar components on the optimal S-CO2 cycle integration are investigated with this model. Finally, linear regression models are established to predict the optimal cp,tot under various conditions of solar component capital cost and design with a deviation less than 2%. Results indicate that the optimal cp,tot is reduced by 8.94% according to the exergoeconomic optimization relative to the conventional thermodynamic optimization. The integration of reheating is not justified for the cycle due to the significant decreased temperature change across the primary heat exchanger and the consequent reduction in the exergoeconomic performance of the SPT plant. Sensitivity analysis highlights the effects of cost and design conditions of solar components on the optimal integration of the S-CO2 cycle, and indicates that the optimal cycle layout may degrade from the recompression cycle to the simple recuperating cycle under certain cost and design conditions of solar components.

A comparison of linear interpolation and spline interpolation for turbine efficiency curves in short-term hydropower scheduling problems

The purpose of short-term hydropower scheduling (STHS) in a competitive electricity market is to maximize the profit in the near future while adapting to the mid-term strategies for system operation. To ensure a realistic and feasible operation for STHS, it is crucial to accurately represent the hydropower production function (HPF). The turbine efficiency can be represented by a two-dimensional table provided by the turbine manufacturers or measured by the hydropower producers on-site with some given efficiency values for a set of net water head and discharge values. In literature, the actual efficiency value for any working point is usually linearly interpolated from the given efficiency values. To the best of authors’ knowledge, no study has been done so far to investigate how a nonlinear interpolation for the turbine efficiency, i.e. spline interpolation, may contribute to the application of STHS. In this paper, we compare the performance of linear interpolation and spline interpolation for turbine efficiency curves in the STHS problem and the bidding strategy in intraday market. The interpolation methods are tested on an operational hydro-power scheduling model, Short-term Hydro Optimization Program (SHOP), which is used by most large hydropower producers in the Nordic countries. Numerical tests demonstrate superior behavior of spline interpolation for the estimation of turbine efficiency between measured points. Compared to linear interpolation, spline interpolation for turbine efficiency curves gives smoother discharge transitions in day-ahead scheduling and is vital for obtaining continuous marginal cost curves in intraday trading. Based on the results in this paper, spline interpolation is a promising alternative to linear interpolation for turbine efficiency curves in STHS.

A Distributed Algorithm for the Cooperative Prediction of Power Production in PV Plants

Forecasting the energy production of photovoltaic plants is today an essential tool for asset owners because it has direct economic implications on the net operating income of the plants whose generated energy is sold in competitive electricity markets. In this paper, we propose an innovative distributed decentralized prediction technique for the forecasting of power generated by several PV plants. The prediction technique is based on the Echo State Networks, which are recurrent neural network models very promising in terms of prediction performance and model accuracy. They will be used in conjunction with a distributed learning algorithm and a distributed consensus protocol that makes unnecessary any central coordinator. The technique has been properly conceived for asset owners that hold a wide portfolio of PV systems that are geographically spread out over large areas. The algorithm reveals very efficient, accurate, and scalable performance to any number of plants and it requires only a simple reliable communication channel. Through a real-world example we assess the applicability of such methodology and show its strength.

On the effects of capacity payments in competitive electricity markets: Capacity adequacy, price cap, and reliability

This paper employs a two-stage model to assess the consequences of an easy-to-use form of capacity payments in competitive electricity markets. We show that capacity payments increase capacity and expected consumer surplus, with little or no effect on expected social welfare (expected consumer surplus plus expected profits minus the cost of using capacity payments minus expected outage costs). In addition, we demonstrate that capacity payments can substantially reduce or, sometimes, fully eliminate electricity outage costs due to price capping or unexpected failure of some generation capacity. Thus, regulators should consider using this form of capacity payments since, in addition to their favorable effect on consumers and on social welfare, they mitigate the ‘missing money’ problem and increase system reliability.

Research on Evaluation Index System of Market Operation for Power Sale Market

With the deepening of the electricity market reform, electricity retail companies have sprung up across the country and the power sale market began to grow. However, the trading rules and operation mode of the power sale market in the initial stage of development are not sound, and the accumulated market experience is not sufficient. Therefore, how to make a reasonable evaluation of the market operation becomes an important and urgent problem. First, the architecture model of the competitive electricity market is studied. Then the trading mode of the competitive electricity market and the relationship and influence among the factors are analysed. Finally, the evaluation index system is constructed on the basis of market operation mode.

Strategic participation in competitive electricity markets: Internal versus sectorial data analysis

Current approaches for risk management in energy market participation mostly refer to portfolio optimization for long-term planning, and stochastic approaches to deal with uncertainties related to renewable energy generation and market prices variation. Risk assessment and management as integrated part of actual market negotiation strategies is lacking from the current literature. This paper addresses this gap by proposing a novel model for decision support of players’ strategic participation in electricity market negotiations, which considers risk management as a core component of the decision-making process. The proposed approach addresses the adaptation of players’ behaviour according to the participation risk, by combining the two most commonly used approaches of forecasting in a company’s scope: the internal data analysis, and the external, or sectorial, data analysis. The internal data analysis considers the evaluation of the company’s evolution in terms of market power and profitability, while the sectorial analysis addresses the assessment of the competing entities in the market sector using a K-Means-based clustering approach. By balancing these two components, the proposed model enables a dynamic adaptation to the market context, using as reference the expected prices from competitor players, and the market price prediction by means of Artificial Neural Networks (ANN). Results under realistic electricity market simulations using real data from the Iberian electricity market operator show that the proposed approach is able to outperform most state-of-the-art market participation strategies, reaching a higher accumulated profit, by adapting players’ actions according to the participation risk.

Short Term Probabilistic Load Forecasting With Integrated Methods

In smart grid era, electric load is becoming more stochastic and less predictable in short horizons with more intermittent energy and competitive electricity market transactions. As a result, short-term probabilistic load forecasting (STPLF) is becoming essential for energy utilities because it helps quantify the risks of decision-making for power systems operation. Currently, probabilistic load forecasts (PLF) are commonly produced from three single components, namely input, model and output. Nevertheless, whether integrating two components to represent dual uncertainties of electric load is practical and able to improve STPLF attracts little regards. To address this issue, this paper proposes three integrated methods by pairwise combination of single representative component, i.e. uniform-biased temperature scenarios (UBTS), quantile regression (QR) and logarithmic residual empirical simulation (LRES). Case study on real utility data demonstrates the superiority of the integrated methods and excavates the relationship between predictive model class and specific integrated method.

Optimal placement of TCSC for congestion management in deregulated power system using firefly algorithm

In today's competitive electricity market, managing transmission congestion is a major challenging issue due to operational constraints. Flexible AC transmission system (FACTS) device can be a choice to control the power flow in the congested transmission lines. This paper explores the use of thyristor controlled series compensator (TCSC) for power flow control in congested network using firefly algorithm (FA). The optimal location of TCSC is identified based on real power performance index and reduction of total system reactive power loss methods. Further in the congestion management (CM) problem, single line outage analysis is also performed. FA is used to determine the minimum total cost which includes production cost and TCSC cost. Results of five bus, IEEE 14 and IEEE 30 bus test systems indicate that FA provides minimum cost compared to the previous literature. The efficiency of the proposed FA for obtaining the high quality solution is also established.

Production Efficiency of Nodal and Zonal Pricing in Imperfectly Competitive Electricity Markets

Electricity markets employ different congestion management methods to handle the limited transmission capacity of the power system. This paper compares production efficiency and other aspects of nodal and zonal pricing. We consider two types of zonal pricing: zonal pricing with Available Transmission Capacity (ATC) and zonal pricing with Flow-Based Market Coupling (FBMC). We develop a mathematical model to study the imperfect competition under zonal pricing with FBMC. Zonal pricing with FBMC is employed in two stages, a day-ahead market stage and a re-dispatch stage. We show that the optimality conditions and market clearing conditions can be reformulated as a mixed integer linear program (MILP), which is straightforward to implement. Zonal pricing with ATC and nodal pricing is used as our benchmarks. The imperfect competition under zonal pricing with ATC and nodal pricing are also formulated as MILP models. All MILP models are demonstrated on 6-node and the modified IEEE 24-node systems. Our numerical results show that the zonal pricing with ATC results in large production inefficiencies due to the inc-dec game. Improving the representation of the transmission network as in the zonal pricing with FBMC mitigates the inc-dec game.