Multifractal based return interval approach for short‐term electricity price volatility risk estimation

Electricity Prices, Large-Scale Renewable Integration, and Policy Implications

European electricity markets are coping with low energy prices as a result of overinvestments in generation capacity, subsidies for renewables and the financial crisis of 2008. In this chapter we explore the implications of low electricity prices on the Swiss electricity market, which is facing the additional challenge of phasing out nuclear power plants and market liberalization. System Dynamics is utilized to model and simulate the long-term impacts on investments in new generation capacity, security of supply and future electricity prices. Simulation results indicate that the current low electricity prices are likely to persist for another decade. The most likely response to the low prices is an underinvestment in generation capacity, with the risk of scarcity pricing under low security of supply, as it coincides with the decommissioning of nuclear power plants. There is little evidence this will lead to boom-and-bust investment cycles. Finally, in the long-term we observe a shift towards renewable energy sources and natural gas fired power plants, resulting in more volatile electricity prices. These findings are similar to earlier studies of the liberalized German and Belgian electricity markets, which are also facing the challenges of a nuclear phase-out under depressed European prices.

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

Volatility of electricity price in Denmark and Sweden

With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region.

The Impact of Intraday Markets on the Market Value of FlexibilityyDecomposing Effects on Profile and the Imbalance Costs

Electricity prices in an interconnected market are influenced by the occurrence of transmission constraints. Until relatively recently, however, the important effects of transmission constraints on both the trajectory and volatility of electricity prices have not played a large role in empirical models of prices. This paper explores the contribution to price volatility in the Queensland electricity market made by transmission constraints. It is found that robust estimation techniques are necessary to guard against incorrect inference in time series models using electricity price data in which severe price spikes occur. The main empirical lesson is that transmission constraints contribute significantly both to the level and variability of price and consequently the performance of a price forecasting model is likely to be improved by incorporating information on transmission constraints. While the general tenor of this conclusion will come as no surprise, the extent and the importance of these effects found in this paper for forecasting price and for computing summary measures like Value-at-Risk serve as a timely reminder to practitioners.

Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options

Data-driven modeling for long-term electricity price forecasting

As variable renewable energy (VRE) sources like wind and solar increasingly enter the electricity spot market, they face challenges due to their unpredictable, fluctuating output, which weakens their market competitiveness. Additionally, the volatility of day-ahead electricity prices adds complexity to VRE market participation. Hydropower, with its flexibility, is often utilized to balance VRE output. In order to address the problems posed by the uncertainty of VRE’s output and day-ahead clearing tariffs for VRE’s participation in the electricity spot market, this study proposes an optimal scheduling model for a joint Hydro-Wind-Solar spot market. The model generates scenario sets for VRE output and tariffs using the Monte Carlo method, which are then reduced via K-means clustering. An optimal dispatch model is developed based on the reduced scenarios, aiming to maximize expected market returns. The model is solved using the Gurobi optimization solver, and its effectiveness is demonstrated using data from a hydropower plant, wind farm, and solar farm in southwest China.

The impact of wind power generation on the electricity price in Germany

Background:Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods:In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results:This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions:Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Demand side load shift has re-emerged as an important tool to solve the problems accompanying the restructuring of power systems, such as capacity shortage, transmission congestion, electricity price volatility and possible reduced system reliability. A methodology to determine demand side load shift is developed to enhance reliability and to reduce nodal price volatility of restructured power systems. In restructured power systems, the conventional same for all customers electricity price is replaced by nodal prices. Electricity prices will interact mutually with demands. This interaction is known as demand response. Demand response is modeled using demand-price elasticity matrix which includes self- and cross-elasticities. The demand side load shifts under N-2 random system contingencies are determined using demand-price elasticity. Then the overall stochastic demand side load shift is calculated using the determined demand side load shifts under random contingencies and their probability. Reliability enhancement and nodal price reduction of restructured power systems due to stochastic demand side load shift are investigated using optimal power flow (OPF) and reliability evaluation techniques. A small but comprehensive reliability test system, RBTS, is used to illustrate the concepts, and simulation results show that stochastic demand side load shift is an efficient tool to reduce nodal price volatility and enhance system and nodal reliability of restructured power systems.

This paper analyzes the impact of wind power generation on the mean and variance of electricity wholesale spot prices in Portugal. The Portuguese market is a natural setting to measure the effect of intermittent renewable generation on electricity prices, as the share of wind in total load demand is one of the largest in the world. Using daily data from 2007 to 2014, we estimate an ARX-EGARCHX model where wind generation and total load demand enter as exogenous explanatory variables for the electricity price. The results show that wind generation reduces the average price level, while it increases the price volatility. Both the mean and volatility effects are substantially stronger for day-time peak prices than for night-time base-load prices.

It is well known that highly volatile control laws, while theoretically optimal for certain systems, are undesirable from an engineering perspective, being generally deleterious to the controlled system. In this article we are concerned with the temporal volatility of the regulator's control process in discrete time Linear Quadratic Regulators (LQRs). Our investigation in this paper unearths a surprising connection between the cost functional that an LQR is tasked with minimizing and the temporal variations of its control laws. We first show that optimally controlling the system always implies high levels of control volatility, i.e., it is impossible to reduce volatility in the optimal control process without sacrificing cost. We also show that, akin to communication systems, every LQR has a Capacity Region associated with it, that dictates and quantifies how much cost is achievable at a given level of control volatility. This additionally establishes the fact that no admissible control policy can simultaneously achieve low volatility and low cost. We then employ this analysis to explain the phenomenon of extreme temporal price volatility, frequently observed in deregulated electricity markets. We model the suppliers and consumers in the market as strategic, price-anticipating participants who submit bids, in the form of price-quantity graphs, to the market operator. We then show that in any such market, at the Nash-Walras equilibrium, improving social welfare must necessarily be traded off with volatility in prices, i.e., it is impossible to reduce volatility in the price of electricity without sacrificing social welfare. Our investigation in this paper unearths a surprising connection between the cost functional that an LQR is tasked with minimizing and the temporal variations of its control laws. We first show that optimally controlling the system always implies high levels of control volatility, i.e., it is impossible to reduce volatility in the optimal control process without sacrificing cost. We also show that, akin to communication systems, every LQR has a Capacity Region associated with it, that dictates and quantifies how much cost is achievable at a given level of control volatility. This additionally establishes the fact that no admissible control policy can simultaneously achieve low volatility and low cost. We then employ this analysis to explain the phenomenon of extreme temporal price volatility, frequently observed in deregulated electricity markets. We model the suppliers and consumers in the market as strategic, price-anticipating participants who submit bids, in the form of price-quantity graphs, to the market operator. We then show that in any such market, at the Nash-Walras equilibrium, improving social welfare must necessarily be traded off with volatility in prices, i.e., it is impossible to reduce volatility in the price of electricity without sacrificing social welfare. We then employ this analysis to explain the phenomenon of extreme temporal price volatility, frequently observed in deregulated electricity markets. We model the suppliers and consumers in the market as strategic, price-anticipating participants who submit bids, in the form of price-quantity graphs, to the market operator. We then show that in any such market, at the Nash-Walras equilibrium, improving social welfare must necessarily be traded off with volatility in prices, i.e., it is impossible to reduce volatility in the price of electricity without sacrificing social welfare.