Long-term Electricity Market Research Articles

Optimal strategies for virtual power plants to participate in medium- and long-term power trading in the context of contracts for difference (CFDs)

Abstract In the context of virtual power plants gradually joining the medium-and long-term power market transactions, the decision-making problem of this new subject in the transaction process has attracted wide attention. Since virtual power plants possess distributed power sources on the supply side, as well as variable loads, energy storage, and other resources on the customer side, they are able to participate in both the sale and purchase of electricity in the medium-and long-term electricity market at the same time, which leads to the need for the relevant analysis to be divided into the two cases of sale and purchase of electricity. At the same time, due to the wide application of the CFD mechanism, power transactions should also take into account the compensation adjustment brought by this mechanism in addition to the market price. To sum up, this paper constructs a static game model for the virtual power plant’s power sales and purchasing strategies in the medium-and long-term power market under the premise of the CFD mechanism. The analysis shows that the optimal trading volume is related to the CFD volume, the total volume of power supplied by the bidding, the market price, etc. The relevant conclusions will help market managers to grasp the market price. The conclusions of the analysis help market managers grasp the key transaction information and assist virtual power plants to participate in medium-and long-term power transactions in an orderly manner.

Research on optimization of power dispatching under the constraint of deviation electricity

In the medium- and long-term electricity market, uncertain power output, and power load will lead to the deviation between the actual generation and the signed medium- and long-term contract electricity. In order to reduce the system deviation balance cost and ensure the balance of power supply and demand, it is necessary to find a suitable dispatch method. First, a monthly electricity decomposition model of thermal power units is proposed in this paper. After that, we propose a power dispatching model under the constraint of deviated electricity quantity to minimize the cost of electricity deviation. The simulation results of numerical examples show that the proposed dispatching model and method can effectively reduce the monthly contract power deviation and the balance cost.

Opinion on intra-provincial medium and long-term electricity market considering available transmission capacity

OPINION article Front. Energy Res., 01 February 2023Sec. Process and Energy Systems Engineering Volume 11 - 2023 | https://doi.org/10.3389/fenrg.2023.1132224

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition, the clean energy of hydropower, wind power and photovoltaic power generation has ushered in great development, but due to the randomness and volatility of its output, it has caused a certain waste of clean energy power generation resources. Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption, this paper establishes a quantitative model of clean energy restricted electricity from the perspective of power system supply and demand balance. Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid- to long-term electricity market and the day-ahead market. Through the multi-scenario analysis method, the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene, and the typical scene is obtained by using the hierarchical clustering algorithm. This paper establishes a electricity retailer’s risk decision model for purchasing and selling electricity in the mid- and long-term market and reduce-abandonment market, and takes the maximum profit expectation of the electricity retailer from purchasing and selling electricity as the objective function. At the same time, in the medium and long-term electricity market and the day-ahead market, the electricity retailer’s purchase cost, electricity sales income, deviation assessment cost and electricity purchase and sale risk are considered. The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies, on the premise of balancing profits and risks.

Cost Diversion Strategies for Pumped-Storage Tariffs for New Power Systems

Pumped-storage plants are the most significant electrical storage component in new power systems and show great potential for scaling up. In this paper, economic costs and benefits have been investigated. Both the costs and benefits can be divided into transmission and distribution tariffs; however, various factors need to be considered to reduce costs in transmission and distribution tariffs. The cost characterization methodology for pumped-storage power plants has been developed. A mathematical model for dispersal through the medium and long-term electricity market, the electricity spot market, the ancillary services market, and the leasing of capacity to scenic power stations has been proposed. A case study has been discussed for a regional grid company and a provincial grid company; also, the effectiveness of dispersal has been analyzed based on the case study. The results indicate that when the spot market and the capacity leasing of new energy sites play the largest role in the diversion, the transmission and distribution prices in Province A can be controlled within 1 cent/kWh by applying various diversion methods.

Integrated modelling of European electricity and hydrogen markets

In 2020, the European Commission published a hydrogen strategy announcing different policy measures to support the construction of a European hydrogen infrastructure. Hereby, a target of 40GW installed electrolyser capacity within the EU by 2030 was set, which will cause a significant electricity demand and will link together hydrogen and electricity markets. Therefore, state-of-the-art power modelling must include both electricity and hydrogen. This work proposes a novel approach to integrate hydrogen in existing long-term optimization electricity market dispatch models. A comprehensive modification of the optimization framework is not necessary as the hydrogen market can be modelled in analogy to the electricity market. This is done by implementing hydrogen markets as additional zones within the model with their own hydrogen demands and production. Thus, a hydrogen layer next to the existing electricity layer is created. Next to the dispatch model, hydrogen generation technologies are integrated into a system dynamics investment module, which is interlinked with the optimization model and determines market-driven investments in power plants based on the NPV. As a support scheme for electrolysers, carbon contracts for difference are implemented. Further, a detailed analysis of the German hydrogen and electricity markets is carried out. The analysis shows that renewable energy sources and electrolysers are complementary technologies, which mutually increase their profitability. The model indicates that higher electricity demands caused by electrolysers will not lead to higher electricity prices but reduce the price volatility.

Designing and analysis of index-based long-term electricity market contract considering recent surge of coal price in China

Under the background that the spot market has not yet been fully established, the risk of coal price fluctuation lacks an effective mechanism to transmit to users. During the period of high coal prices, coal-fired units suffered serious losses and the willingness to generate electricity declined extremely, which was not conducive to the stable growth of the national economy. To solve the above problems, after investigating domestic and foreign solutions, this paper conceived a designing idea for the index-based long-term electricity market contract suitable for the current electricity market. Based on the actual data from a province in China, the specific parameters of index-based contract were designed and the profit of units were analyzed. The results show that the profit fluctuations of the coal-fired units will be significantly reduced after the trading contract adopted. In the end, the key issues of trading contract and the impact on market entities were discussed.

Research on Coupled Cooperative Operation of Medium- and Long-Term and Spot Electricity Transaction for Multi-Energy System: A Case Study in China

Due to the intermittent and anti-peak shaving characteristics of the new energy generator sets, the phenomenon of power abandonment hinders direct participation in the electricity market transactions. The hybrid electricity market can use spot market transactions to absorb renewable energy to a large extent. The multi-energy complementary operation coupling of the hybrid electricity market transactions can exploit the complementation and substitution between different energy sources, realize flexible energy production, consumption, storage, and transmission, and optimize the allocation of resources on a larger scale. In this paper, a mid-long-term spot transaction coordination scheduling (MTCS) model for a multi-energy system is constructed by considering the medium- and long-term electricity market uncertainty and the trial operation characteristics of the spot power market in China. A two-stage solution method is introduced to solve the complex multi-agent, multi-period, and multi-energy model. The results of testing this model on the Gansu region, one of the first eight spot pilot areas in China, are presented and discussed in detail. The results showed that this MTCS model could reduce the opening of thermal power units to a more considerable extent, prioritize the consumption of new energy power generation, and reduce the output uncertainty of new energy through the hybrid power market.

Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers

Price guidance in the flexible energy electricity market is not mature. The imbalance between supply and demand in current electricity markets is mainly caused by uncontrolled flexible energy generation; the electricity supply with load changes in the traditional electricity market is no longer applicable to the current flexible energy electricity market. The work proposes neural network dynamic differential control as the long-term price guidance mechanism for flexible energy service providers. This mechanism alleviates the imbalance between supply and demand in the future electricity market. The neural network dynamic differential control method combines the long-short term memory network and the stochastic dynamic differential method. The proposed method integrates the effects of environmental temperature, holidays, and government regulation on demand. This mechanism enables the demand of flexible energy customers to equal the generation capacity of flexible energy, customers to minimize costs in a long-term electricity market and flexible energy to be completely absorbed. This mechanism directly controls the electrical energy demand and indirectly controls the indoor temperature of the building cooling model through price guidance signals. The experimental results show that the building cooling model can save 4.84% of the cost and 65.03% of the potential cost savings.

Flexibility scores for energy transition pathways: Integrating socio-technical factors in a long-term energy market model

The fundamental energy transition from a fossil-fuel-based energy system towards clean energy is not only about technological change but also about social-technical transition. Using a system dynamics model that includes socio-technical factors and a novel flexibility scoring concept, this paper studies the role of flexibility sources in the European electricity market. Additionally, with the help of the long-term and large-scale electricity market model HECTOR, the study assesses the interplay between the learning rate of a new technology (i.e. carbon capture and storage with flexible sequestration) and the system’s flexibility that will be enhanced by the diffusion of the new flexible technology. Furthermore, the impact of the renewable energy mix on the electricity market’s flexibility is studied by considering the dominance of a specific renewable energy technology (offshore and onshore wind) in the electricity market. The simulation results show that the system’s flexibility can reach higher levels by 2050 if socio-technical parameters are included in HECTOR. A comparison of offshore and onshore wind-oriented scenarios highlights that in the offshore wind-oriented scenario, 3.43 TWh less renewable electricity is generated than in the onshore wind-oriented scenario, and that 16.2 Gigatons more of CO2 are emitted than in the onshore wind-oriented scenario. Overall, it can be concluded that, in the long term, offshore wind provides 9% higher flexibility to the system than onshore wind does.

Medium and long-term electricity market trading strategy considering renewable portfolio standard in the transitional period of electricity market reform in Jiangsu, China

The Energy Consumption Guarantee Mechanism (ECGM) of renewable energy requires the prior consumption ratio of renewable energy, and regulates the proportion of renewable energy power in each province. The implementation of Renewable Portfolio Standard (RPS) will have a great impact on the energy portfolio management and trading strategy of electricity in the medium and long-term electricity market. Grid companies (or power exchange center), as the organizers of the medium and long-term market and the primary entities responsible for renewable energy consumption, need a set of scientific trading strategies for the overall scheduling of inter- and intra-provincial power exchange arrangements. Firstly, based on the medium and long-term power market trading rules of Jiangsu, China, this paper analyzes the risk of uncertainty in the power output of renewable energy, and introduces the optimization model into the cost of risk to schedule the consumption ratio of renewable energy in the province, namely arranging the amount of renewable and non-renewable energy electricity as well as that of electricity outside the province. Secondly, the risk levels and their response are determined, and the electricity arrangements inside and outside the province in the medium and long-term market are updated based on a time window rolling method and the proposed mechanism. Finally, illustrative cases are performed to verify the effectiveness of the model and to provide a reference for the organizers' decision-making of medium and long-term power exchange.

Research on an optimization model for wind power and thermal power participating in two-level power market transactions

In 2015, “Several Opinions on Further Deepening the Reform of the Power System” was issued. The new round of power system reforms proposes to give full play to the vitality of the power generation-side bidding market, which will help to establish a fair, reasonable, and active power market. Due to factors such as a lack of peak shaving capacity and imperfect market mechanisms, China's renewable energy participation in the power market faces many problems. To solve these, this paper proposes a two-level optimization model for a wind power plant and thermal power unit to participate in the medium and long-term electricity market, and day-ahead market transactions. First, the paper proposes the electricity quantity and electricity price determination method of annual bilateral negotiated transactions, monthly centralized bidding transactions and listings, and delisting transactions, and briefly describes how to decompose contract electricity into the day-ahead market in the medium and long-term market. Then, two ways for the wind power plant and thermal power unit to participate in the power market are proposed. With maximization of the market benefits as the objective function, and through comprehensive consideration of the system reserve, wind curtailment penalty, green certificate trading, and other issues, the two models of independent participation and joint participation in market transactions were established to study the problem of maximum profit on the power generation side. Finally, the analysis results of the calculation example show that the joint participation of the wind power plant and thermal power unit in the power market has additional benefits compared to independent participation. The wind power plant can complete the assessment index of the renewable energy quota for the thermal power unit, which does not need to purchase green certificates to complete the assessment indicators. The thermal power unit can provide reserve services for the wind power plant, avoiding output whenever necessary, reducing the cost of the curtailment penalty, and overcoming the threat of wind power plant output fluctuations to the system. The two alliances have greater profit margins.

Analysis of Block Orders and Its Implications for Market Participation Mode of Battery Energy Storage Stations

Flexible block orders designed by European electricity market is a relatively perfect time-sharing electricity price and clearing mechanism. It allows members to provide bidding form of diversification and individuation according to their own characteristics. This mechanism has features of flexibility, compatibility and expansibility, and can reasonably express the trading demands of various types of market participants, which including energy storage. Therefore, it can be used to solve the problem of market participation model of energy storage. Considering the actual situation in China, block orders are suitable for the daily, weekly and monthly markets to help battery energy storage stations. So that they can obtain low-price electric energy by participating in the medium and long-term electricity market and promote the development of them.

Consider medium and long-term electricity market different supply and demand than the sale of electricity price simulation method and system design

After a new round open of electricity sales market, a large number of new sell electricity company involved in the sale of electricity market. Which leads to market instability. Because hair, sale members on both sides of the market in the electric power business behaviour real-time change. So only through the analysis of theory and can’t get the real power market operation state. Based on the analysis of electric power market supply and demand ratio, power suppliers and electric power company in different than the market price of supply and demand, set up to simulate long-term market members under supply and demand than bidding decision-making model, the application of RE - Learning, reinforcement Learning algorithm in 10 power suppliers and 30 sell electricity companies bidding behaviour simulation market under the participation of members. The experimental results show that the use of bidding decision model is a good way to express market members under different supply and demand than market clearing. Which verifies the effectiveness of the proposed method.

A Primer about Storage in Bottom-Up Models of Future Energy Systems. Fundamentals of Storage Operation and Investment in Competitive Long-Term Equilibria

Growing shares of renewable energy sources (RES) in the electricity system increase the need for flexible balancing of supply-dependent feed-in of RES and time-varying demand. Besides flexible, conventional technologies and demand-responses, storage is an important option. Through the use of an analytical approach, this paper explores the implications of the short- and long-run electricity market equilibrium. While conventional and renewable technologies have fixed positions in the supply stack depending on their operational costs, storage may shift in the supply stack over time. Hence, shifts in the supply stack only occur if the storage is either full or empty; otherwise, the so-called shadow price of storage is constant. In a long-term partial electricity market equilibrium, this implies more complicated patterns of operational cash flows for storage technologies. These cash flow patterns will determine, in turn: how storage investments pay off, to what extent storage is part of an efficient energy system, and what power-to-energy ratio should storage have. The implications for future sustainable energy systems with multiple storage technologies are illustrated in a stylized application of Lithium-Ion batteries and pump hydro storage as two possible storage technologies.

Assessing the Benefits of Battery Energy Storage Systems for Frequency Regulation, Based on Electricity Market Price Forecasting

In electricity markets, energy storage systems (ESSs) have been widely used to regulate frequency in power system operations. Frequency regulation (F/R) relates to the short-term reserve power used to balance the real-time mismatch of supply and demand. Every alternating current power system has its own unique standard frequency level, and frequency variation occurs whenever there is a mismatch of supply and demand. To cope with frequency variation, generating units—particularly base-loader generators—reduce their power outputs to a certain level, and the reduced generation outputs are used as a generation reserve whenever frequency variation occurs in the power systems. ESSs have recently been implemented as an innovative means of providing the F/R reserve previously provided by base-loader generators, because they are much faster in responding to frequency variation than conventional generators. We assess the economic benefits of ESSs for F/R, based on a new forecast of long-term electricity market price and real power system operation characteristics. For this purpose, we present case studies with respect to the South Korean electricity market as well as simulation results featuring key variables, along with their implications vis-à-vis electricity market operations.

Integrating Real Options Analysis with long-term electricity market models

In liberalized electricity markets, the investment postponement option is deemed decisive for understanding the addition of new generating capacity. Basically, it refers to the possibility for investors to postpone projects for a period while waiting for the arrival of new and better information about the market evolution. When such development involves major uncertainties, the generation business becomes riskier, and the investors' “wait-and-see” behavior might limit the timely addition of new power plants. In that sense, the literature provides solid empirical evidence about the occurrence of construction cycles in the deregulated electricity industry. However, the strategic flexibility inherent to the option to defer new power plants has not yet been rigorously incorporated to investment signals in existing market models. Therefore, this paper proposes a novel methodology to assess the long-term development of liberalized power markets based on a more realistic approach for valuing generation investments. The work is based on a stochastic dynamic market model, built upon System Dynamics simulation approach. The decision-making framework considers that the addition of new capacity is driven by the economic value of the strategic flexibility associated with deferring investments under uncertainties. Thus, the value of the postponement option is quantified in monetary terms through Real Options Analysis. Simulations confirm the cyclical behavior of the energy-only market in the long run, as suggested by the empirical evidence found in the literature. In addition, sensitivity analysis regarding some relevant exogenous variables depicts an even more fluctuating evolution of the capacity due to the combination of strong demand growth rates with large volatilities. Finally, the model validity is assessed through a formal procedure according to the scope of System Dynamics modeling approach.

Information Shocks and Profitability Risks for Power Plant Investments – Impacts of Policy Instruments

Climate change mitigation requires governmental intervention, but different choices are at hand. While economists in general advocate for first-best instruments, reality looks quite different, with especially many subsidy schemes for renewable energies being used. Supporters of these schemes often argue that investment risk reduction is essential to achieve ambitious environmental targets. In this paper we compare four different instruments (cap, tax, minimum quota and feed-in tariffs/renewable auctions) in terms of efficacy and efficiency and also quantify investment risks, assuming an uncertain investment environment, represented by different information shocks on demand, investment and fuel cost. We use a long-term electricity market equilibrium model (generalized peak load pricing model) of the future German electricity market implemented as a linear optimization problem. Starting from an equilibrium, single input parameters are varied to simulate the arrival of new information. Running the model again with partly fixed capacities then allows us to analyze the adjustment of the power plant portfolio towards the new equilibrium over time. As expected quantity-based instruments are effective in assuring achievement of quantitative goals, notably a certain emission level. Yet risks for investors are rather high in that furthermore that first-best instruments are the most efficient. Risks are lower with price solutions, especially feed-in tariffs or renewable auctions provide the possibility to limit risks extremely by diversification only inside the electricity market.

Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence

The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: (1) a self-developed form of Porter’s five forces analysis (PFFA) determining that electricity markets are characterized by a fairly steady price increase. Dominant driving factors come out to be: (i) uncertainty of future electricity prices; (ii) regulatory complexity; and (iii) generation overcapacities. Similar conclusions derive from (2) a self-developed form of multiple-criteria decision analysis (MCDA). In this case, we find that the electricity market particularly depends on (i) market liberalization and (ii) the European Union (EU)’s economy growth. The applied methodologies provide a novel contribution in forecasting electricity price trends, by analyzing the sentiments, expectations, and knowledge of industry experts, through an assessment of factors influencing the market price and goals of key market participants. An extensive survey was conducted, interviewing experts all over Europe showed that the electricity market is subject to a future slight price increase.

The impact of electricity demand reduction policies on the EU-ETS: Modelling electricity and carbon prices and the effect on industrial competitiveness

The European electricity market is linked to a carbon market with a fixed cap that limits greenhouse gas emissions. At the same time, a number of energy efficiency policy instruments in the EU aim at reducing the electricity consumption. This article explores the interactions between the EU's carbon market on the one hand and instruments specifically targeted towards energy end-use efficiency on the other hand. Our theoretical analysis shows how electricity demand reduction triggered by energy efficiency policy instruments affects the emission trading scheme. Without adjustments of the fixed cap, decreasing electricity demand (relative to business-as-usual) reduces the carbon price without reducing total emissions. With lower carbon prices, costly low emission processes will be substituted by cheaper high emitting processes. Possible electricity and carbon price effects of electricity demand reduction scenarios under various carbon caps are quantified with a long-term electricity market simulation model. The results show that electricity efficiency policies allow for a significant reduction of the carbon cap. Compared to the 2005 emission level, 30% emission reductions can be achieved by 2020 within the emission trading scheme with similar or even lower costs for the industrial sector than were expected when the cap was initially set for a 21% emission reduction.