Electricity Market Model Research Articles

Economic disruptions in long-term energy scenarios – Implications for designing energy policy

The main drivers of the transformation processes affecting electricity markets stem from climate policies and changing economic environments. In order to analyse the respective developments, modelling approaches regularly rely on multiple structural and parametric simplifications. For example, discontinuities in economic development (e.g., business cycles) are frequently disregarded. The distorting effects caused by such simplifications tend to scale up as the time horizons of such analyses increase, and can significantly affect the accuracy of long-term projections. In this study, we include information on economic discontinuities and elaborate on their influences. Based on historical data, we identify the impact of changes in economic parameters and examine their cumulative effect on the German electricity market by applying a techno-economic electricity market model for the period from 2005 to 2014. Similar changes may occur repeatedly in the future, and we expect that a more comprehensive understanding of their effects will increase the validity of long-term scenario assessments. Although dynamic developments have taken place in the past, their effects on such scenarios are regularly ignored. Results indicate that policy decision-making based on modelling frameworks can benefit from a in-depth understanding of the underlying simplifications made in most scenario studies.

Renewable energy support policy evaluation: The role of long-term uncertainty in market modelling

Policy makers are tasked with selecting, designing, and implementing policies to support the transition to a sustainable power system. As part of the task, they often turn to models to quantify and compare the options available to them. In this work, we investigate the importance of the approach to representing long-term uncertainty in the modelling used to evaluate different decarbonisation or renewable support policies. We compare six different policies options; a cap on CO2 emissions (as with a cap and trade scheme), a CO2 price, a renewable capacity target, a green certificates scheme, a renewable generation subsidy, and a renewable capital grant.In a case study of a small power system, we find that using common modelling approaches that attempt to capture uncertainty as multiple different independent scenarios (such as scenario analysis or Monte-Carlo simulation) perform poorly at representing the reaction of a competitive electricity market as measured by a stochastic optimisation model. We find that a policy maker using this approach to make decisions could set a policy 55% more restrictive than required to meet their target.Further, we find that a deterministic model that ignores uncertainty can underestimate carbon abatement costs by up to 86%. Incorporating uncertainty as individual scenarios only slightly improves this result and biases the estimated costs between price and quantity based approaches. Modelling scenarios individually underestimates the cost of quantity targeting policies by up to 66% and overestimates the cost of price based policies by up to 4%.Finally, we find that incorporating uncertainty as individual scenarios results in wind being selected as the most cost efficient technology to respond to decarbonisation policies, on average. However, when considering that decision makers must invest under uncertainty, solar capacity is preferable. The results provide a strong case for the use of the stochastic optimisation approach to incorporating long-term uncertainty into electricity market modelling for renewable energy support policy analysis, despite the additional computational burden.

Insights on Germany’s Future Congestion Management from a Multi-Model Approach

In Germany, the political decision to phase out nuclear and coal-fired power as well as delays in the planned grid extension are expected to intensify the current issue of high grid congestion volumes. In this article, we investigate two instruments which may help to cope with these challenges: market splitting and the introduction of a capacity mechanism. For this purpose, we carry out a comprehensive system analysis by jointly applying the demand side models FORECAST and eLOAD, the electricity market model PowerACE and the optimal power flow model ELMOD. While a German market splitting has a positive short-term impact on the congestion volumes, we find the optimal zonal delimination determined for 2020 to become outdated by 2035 resulting in new grid bottlenecks. Yet, readjusting the zonal configuration would lower the ability of the market split to provide regional investment incentives. Introducing a capacity mechanism with a congestion indicator allows allocating new power plants in regions with higher electricity demand. Consequently, we find the required congestion management to be substantially reduced in this setting. However, given the large amount of design parameters, any capacity mechanism needs to be carefully planned before its introduction to avoid new inefficiences on the market side.

Impacts of Transmission Switching in Zonal Electricity Markets - Part II

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing, and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse, and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system, and comment on the impacts of both proactive, and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market.

Identification of the Efficiency Gap by Coupling a Fundamental Electricity Market Model and an Agent-Based Simulation Model

A reliable and cost-effective electricity system transition requires both the identification of optimal target states and the definition of political and regulatory frameworks that enable these target states to be achieved. Fundamental optimization models are frequently used for the determination of cost-optimal system configurations. They represent a normative approach and typically assume markets with perfect competition. However, it is well known that real systems do not behave in such an optimal way, as decision-makers do not have perfect information at their disposal and real market actors do not take decisions in a purely rational way. These deficiencies lead to increased costs or missed targets, often referred to as an “efficiency gap”. For making rational political decisions, it might be valuable to know which factors influence this efficiency gap and to what extent. In this paper, we identify and quantify this gap by soft-linking a fundamental electricity market model and an agent-based simulation model, which allows the consideration of these effects. In order to distinguish between model-inherent differences and non-ideal market behavior, a rigorous harmonization of the models was conducted first. The results of the comparative analysis show that the efficiency gap increases with higher renewable energy shares and that information deficits and policy instruments affect operational decisions of power market participants and resulting overall costs significantly.

Risks and opportunities for a Swiss hydroelectricity company in a changing climate

Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of individual catchments, and, importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability. Here, we collaborated with Groupe E, a hydroelectricity company operating several reservoirs in the Swiss pre-Alps, and we co-produced hydroclimatic projections tailored to support the upcoming negotiations of their water concession renewal. We started by identifying the vulnerabilities of their activities to climate change; together, we then selected streamflow and electricity demand indices to characterize the associated risks and opportunities. We provided Groupe E with figures showing the projected impacts, which were refined over several meetings. The selected indices enabled us to assess a variety of impacts induced by changes in (i) the seasonal water volume distribution, (ii) low flows, (iii) high flows, and (iv) electricity demand. This enabled us to identify key opportunities (e.g., the future increase in reservoir inflow in winter, when electricity prices have historically been high) and risks (e.g., the expected increase in consecutive days of low flows in summer and fall which is likely to make it more difficult to meet residual flow requirements). We highlight that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. This stakeholder-centered approach, which relies on identifying stakeholder's needs and using them to inform the production and visualization of impact projections, is transferable to other climate impact studies, in the field of water resources and beyond.

Economic analysis of batteries: Impact on security of electricity supply and renewable energy expansion in Germany

Increasing amounts of fluctuating renewable energy lead to decreasing electricity prices and impair security of electricity supply. Consequently, sustainable and economically feasible solutions need to be found to ensure both ongoing renewable energy expansion and stable electricity supply. We examine the impact of batteries on security of the electricity supply and achieving renewable energy expansion. For this purpose we develop an electricity market model that enables the simulation of batteries both as an economic-driven investment option and as a government subsidized option. We present six policy scenarios in which batteries are utilized as an option that is subsidized by the government to secure electricity supply and engender renewable energy expansion. Our simulations, based on empirical data, indicate that, in a free market, battery investments are not profitable for private investors. On the other hand, these six policy scenarios show that by subsidizing investments in batteries governments could ensure a secure electricity supply as well as ongoing renewable energy expansion. A comparison to similar policy scenarios that do not adopt batteries indicates that the total sum of government subsidies and external costs is up to 36% lower when utilizing batteries.

Electric Vehicle Participated Electricity Market Model Considering Flexible Ramping Product Provisions

This article studies electric vehicle (EV) potential to participate in the energy market and provide flexible ramping products (FRPs). EV traffic flows are predicted by the deep belief network, and the availability of flexible EVs is estimated based on the predicted EV traffic flows. Then, a novel market mechanism in distribution system is proposed to encourage the dispatchable EV demand to react to economic signals and provide ramping services. The designed market model is based on locational marginal pricing of energy and marginal pricing of FRPs. System ramping capacity constraints and EV operation constraints are incorporated in the proposed model to achieve the balance between the system social cost minimization and the EV traveling convenience. Moreover, typical uncertainties are considered by the scenario-based approach. Finally, simulations are conducted to verify the effectiveness of the established model and demonstrate the contributions of EVs to the system reliability and flexibility.

ASSESSMENT OF ECONOMIC SECURITY FACTORS OF UKRAINE'S ENERGY MARKET TOWARDS AN INTEGRATED EUROPEAN AREA

The purpose of the article is the evaluation of the economic security factors of Ukraine’s electricity market under conditions of its integration into the European energy area. The implementation of the competitive electricity market model according to the European integration requirements creates transformational challenges for the system of economic interests of energy market participants and their interrelationship. Therefore, the importance of the timely identification and response to economic security factors that may increase the vulnerability of each of the electricity market subjects during the period of structural market adjustment is updated. The research is based on the competitive electricity market model that evolves under the influence of the European integration process. During the course of the present paper, the author has assessed the weight of the economic security factors for Ukraine’s electricity market participants by using an expert evaluation method. Moreover, the statistical method and comparative analysis have been used to investigate the main indicators that reflect the conditions of national electricity markets functioning in the context of economic security were analyzed. In the process of presenting the research results, the authors considered notions of the electricity market and the energy market as fully interchangeable, i.e. synonymous, in order to avoid tautology. In the article, based on the application of the expert evaluation method, subjective and functional structuring of the economic security factors of the Ukrainian energy market was carried out by ranking the weight of their influence on all its participants. The main indicators reflecting the conditions of national electricity markets functioning in the context of economic security were analyzed. Considering the key trends and requirements of the EU energy legislation, the economic security factors were grouped into functional categories, including technical and technological, organizational, informational, financial and economic, fuel and power, environmental, and as well as political and legal factors. Based on the expert evaluation, a map of economic security factors of electricity market participants was formed. It was determined that under conditions of the energy market transformation, organizational factors have the greatest impact on the level of economic security of all its participants. It was found that the most vulnerable among other subjects of the energy market are electricity producers. The scientific novelty of this study is the subject and functional structuring of the economic security factors based on the ranking weight of their impact on all its participants. This provides the possibility to identify and prioritize strategic directions of increasing the level of economic security taking into account the vulnerability of its participants to the manifestation of functional ones. The practical significance of the obtained results is that the structuring of the economic security factors can be used by regulatory authorities in identifying and prioritizing the strategic directions to improve the economic security level of the electricity market in the terms of European integration.

Participation of distributed energy resources and renewable energy resources in different electricity market modes

At present, the domestic power spot market has just started, and the impact of distributed power and renewable energy power generation on power dispatch and market transactions needs further analysis. The electricity market models in Europe, North America and Australia are analyzed in this paper, and their impact on the market participation of renewable energy and distributed power is explained. First of all, the characteristics of each market system are briefly described, and then the transaction data of the current market and the intraday spot are compared and explained. The advantages and disadvantages of various market transactions are analyzed. All the analysis is to provide insight into china’s spot market construction for distributed energy resources and renewable energy resources.

ANALYSIS OF FACTORS FOR FORECASTING ELECTRIC POWER GENERATION BY SOLAR POWER PLANTS


 
 
 The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.
 
 

Joint electricity and carbon market for Northeast Asia energy interconnection

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades. The key challenges for achieving this goal are carbon emission trading and electricity sector regulation, which are also the major components of the carbon and electricity markets, respectively. In this paper, a joint electricity and carbon market model is proposed to investigate the relationships between electricity price, carbon price, and electricity generation capacity, thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework. The proposed model is a dynamically iterative optimization model consisting of upper- level and lower-level models. The upper-level model optimizes power generation and obtains the electricity price, which drives the lower-level model to update the carbon price and electricity generation capacity. The proposed model is verified using the Northeast Asia power grid. The results show that increasing carbon price will result in increased electricity price, along with further increases in renewable energy generation capacity in the following period. This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources, and hence the carbon price will decline. Moreover, the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation. Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050. It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.

Risk Aversion in Multilevel Electricity Market Models With Different Congestion Pricing Regimes

Due to ongoing efforts for decarbonization, electricity markets worldwide are undergoing fundamental transitions, which result in increased uncertainty for all market participants. Against this background, we investigate the impact of risk aversion on investment and market operation in markets with different congestion pricing regimes and multi-level decision making. We develop a stochastic multi-level equilibrium model with risk-averse agents, which includes investment in transmission and generation capacity, market operation, and redispatch. The model can incorporate perfect, as well as imperfect locational price signals and different upper-level expectations about lower-level risk aversion. We apply our model to a stylized two-node example and compare the effects of risk aversion in a system with zonal and nodal pricing, respectively. Our results show that the effect of risk aversion is more pronounced in a market with nodal pricing, compared to a market with imperfect locational price signals. Furthermore, transmission planners that are ignorant about risk aversion of generation companies can induce substantial additional costs, especially in a nodal pricing market.

Vertical bifacial photovoltaics – A complementary technology for the European electricity supply?

Thanks to the two diurnal generation peaks, vertical bifacial photovoltaic power plants (VBPV) with a north-south axis represent an option to meet the challenges of a mismatch between electricity demand and the generation profile of conventional photovoltaic systems (C-PV). Despite this promising characteristic, it is hardly possible to assess the technical and economic properties of VBPV on the basis of existing studies. The present work is a contribution to close this gap. Among other things, the conducted analyses included electricity generation, market revenues, levelized costs of electricity (LCOE) and the cost-reducing effect of VBPV in a national electricity system. C-PV was used as a benchmark in all analyses. Electricity generation was simulated at twelve European sites in four countries.It has been found that above a latitude of 50° VBPV generates more electricity than C-PV. Due to the higher investments, VBPV had higher LCOE at all locations investigated. The results also showed that VBPV can generate higher revenues in electricity markets with a high share of C-PV capacity, but that these additional revenues are not yet sufficient to compensate for the higher investments. With an optimising electricity market model, it was also found that VBPV can contribute to reducing system costs in an electricity system with high shares of C-PV capacity. Against the background of the increasing importance of PV for the electricity supply of most European countries, these findings are of particular importance. In summary, it can be said that VBPV has significant potential at both the business and macro-economic level and that decision makers should promote the market entry of this technology.

How to deal with the risks of phasing out coal in Germany

Abstract Germany has an ambitious climate target for 2030 that cannot be achieved without reducing the high share of coal in power generation. In the face of this, the government has recently decided to directly phase out coal capacity. Yet implementing such a policy comes with two important risks: (1) the decommissioning path might actually be insufficient to reach the 2030 climate target; and (2) the waterbed effect that arises from any additional national policy within the EU Emissions Trading Scheme (EU ETS) cap. In this paper, we quantify these risks using the numerical electricity market model LIMES-EU, and consider options for dealing with them. Our results show that the coal capacity phase out risks missing the 2030 target slightly, but a carbon price floor of at least 35 €/tCO2 would eliminate this risk. Further, we find a substantial waterbed effect, which could be partly alleviated through a carbon price floor coalition of countries, and even fully by cancelling 1.1 GtCO2 of certificates. Yet given the difficulties and challenges that come with either option, members implementing a carbon price floor policy should advocate extending it to the full EU ETS level.

A Novel Peer-to-Peer Local Electricity Market for Joint Trading of Energy and Uncertainty

In the future power system, an increasing number of distributed energy resources will be integrated including intermittent generation like photovoltaic (PV) and flexible demand like electric vehicles (EVs). It has been long thought to utilize the flexible demand to absorb the PV output locally with the technical solution proposed while an effective commercial arrangement is yet to be developed due to the significant uncertainty associated with local generation. This paper proposes a peer-to-peer (P2P) local electricity market model incorporating both energy trading and uncertainty trading simultaneously. The novelty is to match the forecast power with demand having time flexibility and the uncertain power with demand having power flexibility. This market enables more PV uncertainty to be balanced locally rather than propagating to the upper layer system. In the test case, 55.3% of PV forecast error can be balanced locally in the proposed joint market. In comparison, 43.6% of PV forecast error is balanced locally when the forecast power and uncertain power are traded separately in a day-ahead market and a real-time market. The proposed P2P market can also motivate PV owners to improve forecast accuracy.

Supply curves for hydro reservoirs – Estimation and usage in large-scale electricity market models

Hydro electricity generation is of great importance for the current and the future electricity system since it provides electricity without emitting CO2 and moreover hydro reservoirs offer high operational flexibility. With increasing shares of fluctuating renewable energies, their value is even expected to increase, as – depending on the power plant type – they are able to store electricity. Therefore, an adequate representation of hydro power operation in large-scale electricity models is primordial. The aim of this paper is to analyze empirically the operation of large-scale hydro reservoirs based on observed market data. We derive supply curves for hydro reservoirs in Norway based on electricity price and hydro production time series and analyze key influencing factors. To push further, we apply the resulting supply curves in a multi-region electricity market model and show how they may be used to perform historical and counterfactual simulations.

Chile’s Electricity Market Construction and Its Enlightenment

Chile is the first country in the world to implement electric sector reform, and is a country with more advanced renewable energy development in South American countries. This paper introduces Chile's electricity market reform procedure and market model, as well as the power crisis, and summarizes the relevant mechanisms and policies for Chile to stimulate renewable energy development. Finally, the enlightenment of Chile's electricity market reform to China is analyzed.

Advanced hybrid intelligent model and algorithm for de-regulated electricity market

ABSTRACTAn advanced aggressive framework is developed in diverse electricity markets all over the world, which has altered the manner that electric companies yield benefits. Under such circumstance, the companies are supposed to implement required bidding models not only for the sake of attaining reasonable dispatch but also for enhancing benefits. Since the artificial intelligence has found successful in many applications, this paper intends to deploy the hybrid model with the combination of Group Search Optimisation (GSO) and Gravitational Search Algorithm (GSA) termed as Group Search with Gravitational Force (GSGF) model to meet demand-side management principles. By considering the parameters under deregulated environment, the proposed model solves the unit commitment problem. Accordingly, this paper designs the bidding model of IEEE-30 and IEEE-75 test bus system with appropriate bidding coefficient, which appears to attain a high profit. Further, it analyses the performance of both the test bus systems by evaluating total profit, consumed bidding power, statistical report of cost and Market Clearing Price (MCP). In the analysis, it compares the performance of proposed GSGF model with the traditional GA (Genetic Algorithm), ABC (Artificial Bee Colony), PSO (Particle Swarm Optimisation), GSA and GSO bidding models. From the experimental results, the profit of the proposed model is higher than the conventional model, thus attains maximum performance.

Estimating fast mean-reverting jumps in electricity market models

Based on empirical evidence of fast mean-reverting spikes, electricity spot prices are often modeledX+Zβas the sum of a continuous Itô semimartingaleXand a mean-reverting compound Poisson processZtβ=∫0t∫ℝxe−β(t−s)p̲(ds,dt) wherep̲(ds,dt) is Poisson random measure with intensityλds⊗dt. In a first part, we investigate the estimation of (λ,β) from discrete observations and establish asymptotic efficiency in various asymptotic settings. In a second part, we discuss the use of our inference results for correcting the value of forward contracts on electricity markets in presence of spikes. We implement our method on real data in the French, German and Australian market over 2015 and 2016 and show in particular the effect of spike modelling on the valuation of certain strip options. In particular, we show that some out-of-the-money options have a significant value if we incorporate spikes in our modelling, while having a value close to 0 otherwise.