Electricity Spot Market Prices Research Articles

Impact of the New Electricity Remuneration Scheme on the Waste-to-Energy Recovery Activity in Portugal

The remuneration scheme for the electricity produced by Waste-to-Energy (WtE) recovery plants has changed recently in Portugal according to 2020 legislation. The new model, linking the electricity remuneration from WtE plants to the spot electricity prices, is expected to bring greater uncertainty in the waste activity, which is a novelty for the sector. In Portugal, Valorsul is the municipal waste treatment entity responsible for the recovery and treatment of municipal solid waste (MSW) produced in 19 municipalities in the Lisbon area. This paper highlights the impact of the new Portuguese electricity remuneration scheme for electricity from waste on Valorsul’s WtE plant. For this purpose, the new remuneration scheme is modeled and simulated based on electricity spot market price scenarios, which are compared with the base case scenario of the former remuneration scheme. Considering different electricity prices for the electricity produced by the WtE plant, the present study anticipates the consequences of the gate-fee of such regulatory changes. Results show that any price changes in the electricity remuneration scheme are offset by equivalent changes in the waste gate-fee. Consequently, the change in the remuneration of the electricity from the WtE plant is, in fact, neutral for the Valorsul accounts and lower revenues from the electricity generation activity of the WtE will negatively impact the gate-fee prices paid by the waste users.

Electricity trading for coal-fired power plants in Turkish power market considering uncertainty in spot, derivatives and bilateral contract market

In deregulated power markets, electricity suppliers have the option to trade in the spot market, derivatives market, and bilateral contract market. The spot market is always available and open to competition, but the variability and risk incurred need to be carefully handled. The suppliers might allocate their capacity in the derivatives and bilateral contract market if these alternatives are more viable. The strike price, bilateral contract price, and spot market prices need to be used to decide the capacity allocation problem considering the generation cost of the supplier. This paper first examines the market design and electricity trading in the Turkish electricity market. Then three problems were proposed for a coal-fired coal unit that aims to allocate its capacity to spot, derivative, and bilateral contract markets to maximize its expected profit. A Monte Carlo method is used for allocated electricity capacities, spot market, strike, and bilateral contract price scenarios. A simulation methodology is then proposed that includes capacities allocated to each market and price scenarios. The best capacity allocation strategy is determined that return the highest expected profits for all market price samples. The model is illustrated for a coal unit in the Turkish electricity market. The results are presented for the case, including 100 spot price samples, 100 capacity scenarios, 3 scenarios for the strike, and bilateral contract prices. The sensitivity analysis for spot price volatility on the profit is also presented with 20% volatility increase. It is shown that allocating the capacity to more than one market can increase the total expected profit for a power supplier and the rate of increase varies depending on the scenario set.

Smart Scheduling of Pump Control in Wastewater Networks Based on Electricity Spot Market Prices

Smart Scheduling of Pump Control in Wastewater Networks Based on Electricity Spot Market Prices

Optimal contracts for renewable electricity

Companies are increasingly choosing to procure their power from renewable energy sources, with their own set of potential challenges. This paper characterizes the contracts that minimize the cost of procuring a given amount of renewable energy from two risk averse, energy generators who are inherently unreliable (such as wind and solar). We contrast outcomes arising when investments are set in centralized and decentralized settings, with the absence of reliability addressed by either issuing orders in excess of what is needed or by investing in improved reliability. Our results suggest that future contracts may be geared towards a greater reliance on order inflation and lower investments in reliability as the cost of renewable energy keeps falling. The implications of these results for grid congestion and electricity spot market prices should be of interest to regulators and transmission system operators. • Renewable power procurement with bilateral contracts is growing but under-studied • We analyse optimal contracts with electricity generators from variable renewable energy sources • We compare results for centralized and decentralized power generation • Depending on the setting, bilateral contracts for both centralized and decentralized power generation may induce order inflation rather than investment in reliability • For decentralized power generation, we find that increasing order sizes trigger countervailing incentives to invest in reliability

Electricity Price Forecasting with Neural Networks on EPEX Order Books

ABSTRACT This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected total demand. Appropriate feature extraction for the order book data is developed proceeding from existing literature. Using cross-validation to optimize hyperparameters, neural networks and random forests are fit to the data. Their in-sample and out-of-sample performance is compared to statistical reference models. The machine learning models outperform traditional approaches.

Optimal Timing of Onshore Wind Repowering in Germany under Policy Regime Changes: A Real Options Analysis

In this paper, we investigate capacity expansion, the technological development of wind turbine generators, and the merits of onshore wind repowering in Germany. We analyze the regulatory framework that is presently in place and its impact on the profitability of the onshore wind parks commissioned under previous regulatory frameworks. The optimal timing of repowering under today’s market premium model is scrutinized. Repowering in a market without subsidies for onshore wind energy is also evaluated. In the two cases (regimes) investigated, the electricity price is modeled stochastically as a geometric Brownian motion process. More specifically, using a real options modeling framework for investments under a free market regime, we analyze how the regime change affects the optimal timing of repowering. Further, we check the results for their robustness via a sensitivity analysis for both analyzed regimes. We find that repowering well before the plant’s expected end of life (‘early repowering’) can be economically preferable under the German Renewable Energy Sources Act 2017 remuneration scheme and that, due to the electricity spot market price development, repowering under the market premium regime is more profitable than it is under the free market regime. The economic viability is strongly influenced by the duration of the initial tariff granted to the old installation, the expected digression factor of the future reference tariff, and the increase in electricity generation achieved through repowering.

Price arbitrage using variable-efficiency energy storage

Necessary conditions for conducting arbitrage on electricity spot market prices are presented for large-scale, generic energy storage devices, as a function of device power, conversion efficiency, and per-period operating costs. A mixed integer linear program (MILP) is formulated that uses piecewise-affine approximations of potentially nonlinear energy conversion efficiency to maximize arbitrage over a fixed period. By initially removing storage limit constraints, the optimal solution to the MILP is shown to be a threshold policy, with device operating power a monotonic function of price level. The initial assumption of no storage limits is then revisited. The objective is found to be relatively insensitive to device storage limits, provided the storage size is within an order of magnitude of the optimal storage size for the given problem data.

Radioinactive: Do nuclear power plant outages in France affect the German electricity prices?

Do nuclear power plant outages in France affect the German electricity prices? In the case of the extensive inspections from October 2016 to February 2017 in 12 French nuclear power plants: Yes. This capacity shock increased the French electricity spot market price on average by 27.1%. The German-Austrian electricity spot market price was affected through cross-border trade. Due to increased electricity exports from Germany to France, the German-Austrian electricity spot market price increased on average by 3.2%. Moreover, due to decreased electricity imports from France to Germany, the German-Austrian electricity spot price increased on average by 7.6%. These results derive from a quasi-experimental approach based on coarsened exact matching, where the exogenous nature of the capacity shock is exploited as a random treatment in order to identify causal effects.

A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market

The electricity spot market is now being implemented in China. Demand response, as a kind of flexible resource, is also being studied and explored for the constructed power market. Among the many demand response applications, the virtual power plant (VPP) as an aggregator of distributed energy resources (DERs), receives ever-increasing attention. However, the participation manner and related impacts of the VPP to the electricity spot market are still unknown within the current power market rules. Under this background, obeying the present trading rules of China’s electricity spot market, a two-stage dispatching model with optimized bidding and operating strategy in the day-ahead (DA) and real-time (RT) market for the VPP is proposed. In the designed model, the conditional risk value (CVaR) is adopted to address the risk encountered by the uncertainty of the electricity spot market price. The impact of the user-side over-deviated revenue mechanism (UORM) of the China spot market on the income of the VPP in the DA and RT market is also analyzed. For a full evaluation, different coefficients for the influence of DA and RT risk, UORM, and energy storage system (ESS) are tested to investigate their respective impacts on the revenue of the VPP. The simulation cases prove that the proposed method is helpful for the VPP to optimize DERs’ output in the electricity spot market according to its own risk preference.

Lifetime Extension, Repowering or Decommissioning? Decision Support for Operators of Ageing Wind Turbines

In Germany, more than one third of the installed wind energy capacity will leave the feed-in tariff funding between 2021 and 2025. Operators of affected turbines are therefore increasingly concerned with the design of profitable end-of-funding strategies. This requires feasibility analyses of both lifetime extension and repowering options and entails the subsequent challenge to determine the optimal lifetime extension and corresponding repowering timing. To support operators and other stakeholders dealing with wind turbines’ end-of-life issues, this study presents a geographic information system that permits evaluating optimal end-of-funding strategies at different spatial scales reaching down to detailed analyses on individual turbine level. The decision support system processes topographic, wind, turbine, and finance data in an integrated system of resource simulations, spatial planning analyses and economic viability assessments. Case-study results show that a uniform end-of-funding strategy cannot be applied to all ageing turbines. Conducted sensitivity analyses rather indicate that the best strategy highly depends on various turbine-specific aspects, especially the location, type and maintenance costs as well as exogenous factors, including the developments of electricity spot market prices and tendered feed-in premiums. In light of latest trends regarding the exogenous factors, lifetime extension and repowering potentials increase. However, the results also indicate that dismantling, disposal and recycling of numerous ageing turbines will become a major challenge for the wind energy sector in the next decade.

Model-based bidding strategies on the primary balancing market for energy-intense processes

Energy-intense enterprises that flexibilize their electricity consumption can market this either at electricity spot markets or by offering ancillary services on demand, such as balancing power. We formulate optimization of the balancing power bidding strategy as a mixed-integer nonlinear program considering both price forecasts for the ancillary service market and hourly varying spot market prices. We solve this two-stage approach by decomposition into a nonlinear bidding problem and a mixed-integer linear scheduling problem. We consider aluminum electrolysis participating in the German primary balancing market. We show savings in weekly production costs of 5–20% compared to stationary operation. The savings due to the optimal bidding strategy are up to twice the savings from pure exploitation of electricity spot market price spreads. We thus demonstrate that energy-intense processes can systematically take advantage of highly profitable demand-side management measures beyond a spot market price adjusted production.

Electricity market short‐term risk management via risk‐adjusted probability measures

This study presents an iterative algorithm for modelling the mean-risk model with the conditional value at risk (CVaR). The algorithm is based on the Lagrangian relaxation decomposition, and its main advantage is that it allows removing the coupling between the scenarios due to the constraints used to model the risk. At each stage of the algorithm, a risk-neutral stochastic optimisation problem is solved with the risk-adjusted probabilities that substitute the original ones. The study presents the application of the proposed iterative CVaR algorithm to two different short-term problems where the decision makers are exposed to a high volatility of electricity spot market prices. In the first problem, a time horizon of 1 week is taken into account and a future physical contract is employed as a hedging mechanism. The second problem includes a very detailed formulation of the unit commitment problem. The numerical application is based on realistic data of the Iberian electricity market, where the algorithm has shown a good performance in terms of accuracy and computational time. In addition, this study provides a criterion for selecting the value of the parameters used to implement the CVaR model.

Towards the Interactive Effects of Demand Response Participation on Electricity Spot Market Price

Abstract The electricity market is threatened by supply scarcity, which may lead to very sharp price spikes in the spot market. On the other hand, demand-side’s activities could effectively mitigate the supply scarcity and absorb most of these shocks and therefore smooth out the price volatility. In this paper, the positive effects of employing demand response programs on the spot market price are investigated. A demand-price elasticity based model is used to simulate the customer reaction function in the presence of a real time pricing. The demand achieve by DR program is used to adjust the spot market price by using a price regression model. SAS software is used to run the multiple linear regression model and MATLAB is used to simulate the demand response model. The approach is applied on one week data in summer 2014 of Connecticut in New England ISO. It could be concluded from the results of this study that applying DR program smooths out most of the price spikes in the electricity spot market and considerably reduces the customers’ electricity cost.

Assessment of RES technology market values and the merit-order effect – an econometric multi-country analysis

This study presents an assessment of both the merit-order effect and the market values of electricity generated from variable renewable energy sources, namely wind and solar photovoltaics. The historical price development in several European countries – that cover 73% of the renewable energy source share in Europe’s regional electricity markets – has been taken into account. To gain insights into the impact of renewable electricity on prices, market values and the merit-order effect were calculated using a multivariate regression analysis and ex-post calculations. All the countries analyzed show a consistent, negative impact of renewable electricity on electricity spot market prices and a decreasing market value of renewable energy source, possibly attributable to increased shares. The coefficients are economically and statistically significant. This study provides insights into a large geographical spread of European electricity markets, enables a comparison between countries, and therefore has valuable implications for policy makers.

The potential of decentralized power-to-heat as a flexibility option for the german electricity system: A microeconomic perspective

One fundamental challenge of the German energy transition for the electricity market is the growing imbalance between inflexible generation and generally inelastic load. Against this background, we investigate the future potential of decentralized Power-to-Heat (P2H) as an additional demand-side flexibility option for the German electricity system. Precisely, we analyze the case of hybrid systems, where conventional gas and oil condensing boiler systems are equipped with an electric heating rod. In contrast to previous approaches, we set our focus on the economic incentives at household level: only if the switch to a P2H-hybrid system can reduce individual heating expenses significantly, a considerable number of these systems will be installed. For this purpose, we implemented an integrated approach combining three distinct simulation modules. First, a stochastic simulation of the electricity spot market prices in 2020 was conducted. Second, average heat load profiles were generated based on a standard bottom-up analysis. Both results were then fed into an optimization model calculating the cost-minimizing paths of heat generation at household level during the year 2020. The simulated annual savings prove modest as long as household electricity prices are not heavily reduced through political influence.

Competition in Germany’s Minute Reserve Power Market: An Econometric Analysis

The German reserve power market has been subject to important regulatory changes in recent years. A new market design has been introduced by synchronizing and interconnecting the four regional German control areas. We analyze whether the reforms have led to lower prices for minute reserve power (MRP). In contrast to existing papers, we use a unique panel dataset to account for unobserved heterogeneity between the four German regional markets. Moreover, we control for endogeneity by using weather data as instruments for electricity spot market prices. We find that the reforms were jointly successful in decreasing MRP prices leading to substantial cost savings for the transmission system operators.

Optimal Design of Feed-in-Tariffs to Stimulate Renewable Energy Investments Under Regulatory Uncertainty - A Real Options Analysis

Feed-in-tariffs (FITs) are widely used as policy instruments to promote investments in renewable energy sources (RES). While FITs are often regarded the most effective RES support scheme, regulators around the world continuously review their FIT schemes in the light of budget constraints and evolving policy goals. We assess the impact of adjustments to FIT schemes by quantifying the relationship between FIT levels, i.e. the guaranteed amount paid per quantity of electricity produced, and the propensity to invest in RES. Through a regime switching model, we quantify the impact of regulatory uncertainty induced by regulators considering moves from a FIT scheme to a more market-oriented regulatory regime. Our focus is on market-independent, fixed FITs, the dominant scheme in Europe receiving increasing attention globally. We find that RES investment projects under market-independent, fixed FIT schemes become now-or-never decisions and derive FIT thresholds required to induce investment. We show that uncertainty regarding future regulatory regimes only alters investment behavior for FIT levels near electricity spot market prices and high probabilities of an imminent regime switch.

The impact of emissions trading on electricity spot market price behavior

PurposeUnder the Kyoto protocol, emissions trading was imposed upon the Nordic Nord Pool Spot market in 2005. The purpose of this paper is to identify and characterize an important side‐effect of emissions trading on electricity spot market price behavior by statistically comparing price behavior before and after emissions trading was introduced.Design/methodology/approachThe analysis is based on an analysis of the skill of regression models in explaining price behavior before and after 2005.FindingsIt turns out that regression models based on background variables such as temperature, water reservoir levels, and even the price of emission rights themselves lose much of their skill from 2005 onwards. The histogram of the residual time series of an optimally calibrated regression model demonstrates a considerably more “fat‐tailed” behavior after 2005, with a much higher volatility and reduced amenability for regression by background variables.Practical implicationsThe results point to an increased medium‐ and long‐term uncertainty in the Nordic electricity spot market, brought about by emissions trading as an unintended side‐effect. It seems emissions trading has introduced a stronger “psychological” component into price behavior, increasing its volatility and making it prone to more frequent price spikes. This has made the electricity market more difficult for market managers and regulators to manage.Originality/valueThe paper presents the first statistical attempt to quantify the way electricity spot price dynamics have changed in Europe after starting the Emissions Trading Scheme based on the Kyoto protocol.

The multiple-mean-reversion jump-diffusion model for Nordic electricity spot prices

Electricity spot market prices are notoriously difficult to model, let alone predict, because of their extreme volatility. Such volatility is reflected in so-called price spikes that may increase the spot price by an order of magnitude in a matter of hours. Spot market price series are also subject to many other types of phenomena, such as periodicities at different scales, and to mean reversion. We introduce a model for electricity spot market prices that includes both spikes and mean reversion. The model is based on a jump-diffusion process that is superimposed on a mean-reverting Ornstein-Uhlenbeck model. Mean reversion takes place at several different time and price scales, so as to reproduce the observed behavior of spot market prices correctly. The parameters of the model are calibrated with the Nord Pool spot market hourly price series using a maximum likelihood approach. The simulated price series obtained in this way very closely follows the statistical characteristics of the real price series.

The impact of wind generation on the electricity spot-market price level and variance: The Texas experience

The literature on renewable energy suggests that an increase in intermittent wind generation would reduce the spot electricity market price by displacing high fuel-cost marginal generation. Taking advantage of a large file of Texas-based 15-min data, we show that while rising wind generation does indeed tend to reduce the level of spot prices, it is also likely to enlarge the spot-price variance. The key policy implication is that increasing use of price risk management should accompany expanded deployment of wind generation.