Fluctuating Electricity Prices Research Articles

Research on power system flexibility considering uncertainties

In order to help achieve the goal of carbon peak and carbon neutrality, the large-scale development and application of clean renewable energy, like wind generation and solar power, will become an important power source in the future. Large-scale clean renewable energy generation has the uncertain characteristics of intermittency, randomness, and volatility, which brings great challenges to the balance regulation and flexible operation of the power system. In addition, the rapid development of renewable energy has led to strong fluctuations in electricity prices in the power market. To ensure the safe, reliable, and economic operation of the power system, how to improve the power system flexibility in an uncertain environment has become a research hotspot. Considering the uncertainties, this article analyzes and summarizes the research progress related to power system flexibility from the perspective of power system planning, operation, and the electricity market. Aiming at the modeling technology of uncertainty, the related modeling methods including stochastic programming, robust optimization, and distributionally robust optimization are summarized from the perspective of mathematics, and the application of these methods in power system flexibility is discussed.

Flexible Short-Term Electricity Certificates—An Analysis of Trading Strategies on the Continuous Intraday Market

The most important price for short-term electricity trading in Germany is the day-ahead auction price, which is provided by EPEX SPOT. Basically, short-term fluctuating electricity prices allow cost-optimized production planning by shifting electricity-intensive processes to times of favorable electricity prices. However, the day-ahead price as the outcome of an auction is not directly tradeable afterwards. We propose short-term flexible electricity certificates that pass on the day-ahead auction prices plus a premium for the supplier, enabling users to plan electricity consumption based on realized day-ahead auction prices. We analyze the supplier’s problem of delivering electricity based on such certificates. The supplier can adjust the required electricity volume after the close of the day-ahead auction on the continuous intraday market. We analyze the price fluctuations in this market in relation to the day-ahead price and propose different trading strategies. Using the order book history of EPEX SPOT, we analyze the trading success and risk of these strategies. Furthermore, we investigate to what extent trading success can be explained by changes in market conditions, and, in particular, we identify renewable forecast errors as a driver.

The demand response potential in copper production

Demand response (DR) of large industrial electricity consumers is a promising option to balance the fluctuating supply by renewable energies in the electricity grid. Renewable energy technologies themselves depend on copper as a key material. At the same time, the production of copper is a power-intensive process but its DR potential has not yet been quantified in detail via process scheduling. Here, we analyze the DR potential of copper production by optimally scheduling the batch and continuous tasks of a representative copper process. To determine the optimal schedule, we formulate a mixed-integer linear program (MILP) based on the resource-task network (RTN) formulation approach. We first optimize the production volume to define a reference case and then minimize the electricity costs under time-varying electricity prices while retaining the production target. A sensitivity analysis evaluates the impact of task capacities on the production volume and DR potential. The results indicate a significant DR potential, as optimal scheduling can reduce the annual electricity costs by up to 14.2% while still producing the maximum copper output as the reference process schedule. The power-intensive electrolytic refining shows the largest potential for reducing costs. Offgas handling, air separation, and air compression further show significant cost reduction potentials. These tasks must process large material streams that are directly connected to operating the smelting and refining tasks. Our model shows the potential of considering these interlinked tasks in one scheduling model that focuses on DR. The results suggest that DR scheduling in copper production has a significant economic potential without compromising production goals. Further, the DR scheduling shifts large amounts of electricity demand by responding to fluctuating electricity prices, which enables flexibility of the demand side and can thus support the integration of renewable energy into the electric grid. • Process scheduling of copper production to assess demand response (DR) potential. • Discrete-time resource-task network model to resolve electricity demands. • DR can reduce electricity costs by up to 14% based on German day-ahead market. • Substantial load shifting potential of 16% of the annual electricity demand. • Task-specific DR potentials and impact of process debottlenecking analyzed.

Electricity Price Instability over Time: Time Series Analysis and Forecasting

Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by applying time series analysis. The determinants of electricity price fluctuations are broken down into three groups: exogenous prices (gas, coal and CO2 prices), internal (consumption and generation) and external (net import between neighboring bidding zones) electricity flows. Based on the SARIMAX model, we tried to combine all these factors to forecast electricity prices in the single bidding zone. It was found that the SARIMAX (1, 1, 2) × (3, 1, 0, 7) model with exogenous prices, internal and external electricity flows, which has the lowest AIC and MAPE values, is the best-fitted model for the DE-LU bidding zone. Anonymous trading and unpredictable individual bidding strategies lead to persistent price volatility, which causes electricity prices to deviate from fundamental trends. To reveal the risk factors, the SARIMAX model of electricity prices needs to be supplemented with a GARCH model of the residual returns. For forecasting electricity price residual volatility in the DE-LU bidding zone, the SARIMAX model with exogenous prices, internal and external electricity flows must be accompanied with the GARCH (7, 0) model.

Model predictive approaches for cost-efficient building climate control with seasonal energy storage

Due to the increasing share of fluctuating renewable energy, a seasonal energy storage with a high capacity is needed, which stores the summer surplus electricity to satisfy the winter heating demand.It is assumed that the control integration of a novel thermochemical seasonal energy storage concept into a building energy system can meet this requirement and save operating costs. For this purpose, a model-predictive control concept with a prediction horizon beyond that of public weather forecasts is crucial.A state-based model is developed consisting of a building, a water buffer and a heat supply. To assess the effects of seasonal storage, different heat supply configurations are considered. On this basis, two Model Predictive Control (MPC) concepts are designed to efficiently operate the system over one year. Since public weather forecasts are reliable in the time range of several days, test reference year data are used to approximate the weather forecast beyond the public forecast period. Additionally, the control hierarchy is comprised of a superordinate optimal generation scheduling (OGS) and a subordinate MPC. The concepts follow the scheduled long-term lime storage trajectory and realise possible short-term yields based on the current public forecast. The trajectory tracking is formulated either in the objective function or the constraints.The integration of the novel lime storage module into the heat supply of a building allows a reduction of operating costs of 18% in the realistic scenario and up to 80% in case of highly fluctuating electricity prices. This reduction potential is fully exploited by the developed control approaches, but it is very sensitive to the change of the controller parameters, the fluctuation of the electricity price and the weather data. Moreover, by applying the objective-based reference tracking approach, the higher-level scheduling hierarchy could be avoided.Through the results of this work, it is confirmed that the integration of a seasonal energy storage system can greatly reduce annual operating costs and that it is not crucial to apply a prediction horizon beyond that of public forecasts.

Optimization of hydrogen supply from renewable electricity including cavern storage

Abstract The present study introduces a methodology to model electricity based hydrogen supply systems as a Mixed Integer Linear Programming (MILP) problem. The novelty of the presented approach lies especially in the linear formulations of the models for electrolysis and salt cavern storage. The proposed linear electrolysis model allows for an accurate consideration of operating limits and operating point-specific efficiencies, while the two-dimensional cavern model treats the cavern volume as a decision variable. The developed formulations are implemented in the open energy modeling framework (oemof) and applied to representative case studies with 2020 marginal conditions. Thereby, it has been confirmed that the individual consideration of power supply and hydrogen demand is crucial for optimal system design and operation. If electricity is drawn exclusively from the German grid, hydrogen costs of 2.67 € kg H 2 − 1 $€{\text{kg}}_{{\text{H}}_{2}}^{-1}$ are identified along with an increased CO2 footprint compared to natural gas based hydrogen. By contrast, a significantly reduced CO2 footprint results from autarkic wind power supply at costs of at least 4.28 € kg H 2 − 1 $€{\text{kg}}_{{\text{H}}_{2}}^{-1}$ . Further findings on autarkic operation include optimal ratios of electrolyzer and wind farm nominal power, as well as power curtailment strategies. Evidence is provided that salt cavern interim storage is beneficial. With grid connection, it serves to exploit electricity price fluctuations, while with renewable autarkic operation, it is essential to compensate for seasonal fluctuations in generation.

Optimal scheduling strategy of electricity‐heat‐hydrogen integrated energy system under different operating modes

With the increasing penetration level of renewable energy in the integrated energy system (IES), water electrolysis for hydrogen production (WEHP) technology has become an effective solution to increase the capacity of renewable energy utilization. Therefore, this paper proposes to take WEHP and hydrogen fuel cell (HFC) as flexible resources to participate in the optimal scheduling of IES under grid-connected/off-grid operation mode. Firstly, focusing on the production, storage and utilization of hydrogen energy, a unified operation model of WEHP module is built by analyzing the differences of the three WEHP technologies. And then, “co-generation” model of HFC, the residual capacity and transportation cost model of hydrogen storage device (HSD) are built. Second, by analyzing the uncertainty of light intensity and wind speed, a prediction error correction method is proposed to improve the prediction data accuracy of renewable energy power generation. Third, taking the lowest daily operation cost as the objective function, the optimal scheduling model of electricity-heat-hydrogen integrated energy system under grid-connected/off-grid operation mode is constructed. On this basis, the operation scheduling model of electricity-heat-hydrogen integrated energy system is transformed into a mixed integer linear model by piecewise linearization method, and the model is solved by CPLEX solver. Finally, an example is given to verify the rationality of the proposed scheduling model in the grid-connected/off-grid operation mode. On one hand, reasonable optimal scheduling of multiple WEHP combination is helpful to reduce the daily operation cost of IES. On the other hand, by correcting the prediction data deviation of renewable energy power generation, the equipment output of electricity-heat-hydrogen integrated energy system can be more reasonable, which is helpful to reduce the daily operation cost and improve the operation reliability. The effects of sensitive factors such as the penetration level of renewable energy, the relative fluctuation of electricity price and hydrogen price on the operation cost of optimal scheduling scheme are further analyzed, which provides a theoretical reference for the optimal scheduling of IES.

Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity of placed sensors in a district heating system make the operational use of pipeline energy storage challenging. A vast majority of previous works determined a control strategy by a decomposition of a mixed-integer nonlinear model and significant simplifications. To mitigate consequential stability, feasibility, and computational complexity challenges, we model CHP economic dispatch as a Markov decision process. We use a reinforcement learning (RL) algorithm to estimate the system’s dynamics through interactions with the simulation environment. The RL approach is compared with a detailed nonlinear mathematical optimizer on day-ahead and real-time electricity markets and two district heating grid models. The proposed method achieves moderate profit impacted by environment stochasticity. The advantages of the RL approach are reflected in three aspects: stability, feasibility, and time scale flexibility. From this, it can be concluded that RL is a promising alternative for real-time control of complex, nonlinear industrial systems.

Reversible Power-to-Gas systems for energy conversion and storage

In the transition to decarbonized energy systems, Power-to-Gas (PtG) processes have the potential to connect the existing markets for electricity and hydrogen. Specifically, reversible PtG systems can convert electricity to hydrogen at times of ample power supply, yet they can also operate in the reverse direction to deliver electricity during times when power is relatively scarce. Here we develop a model for determining when reversible PtG systems are economically viable. We apply the model to the current market environment in both Germany and Texas and find that the reversibility feature of unitized regenerative fuel cells (solid oxide) makes them already cost-competitive at current hydrogen prices, provided the fluctuations in electricity prices are as pronounced as currently observed in Texas. We further project that, due to their inherent flexibility, reversible PtG systems would remain economically viable at substantially lower hydrogen prices in the future, provided recent technological trends continue over the coming decade.

Enhanced-decision energy trading for island renewable microgrids considering large interruptible refrigeration load intra-hour switching

Large interruptible refrigeration load (LIRL) can be incorporated into the scheduling model of microgrid (MG) to optimize the operational cost. However, based on a classic 1-hour step, the LIRL is either turned on or off, resulting in unsatisfactory results. Although a smaller scheduling step can adjust the LIRI more finely, the computational burden will be increased simultaneously. To address the problem, this paper proposes a novel enhanced-decision energy trading strategy for renewable MGs. First, enhanced decisions are proposed, where on-state and off-state durations of LIRL in an hour instead of on or off state would be determined. On the basis, based on a 1-hour scheduling step, an enhanced-decision trading model is presented, where the energy operator maximizes the electricity sale profit, and load aggregators (LA) minimize the electricity usage cost. This optimization model is first converted into a classic mixed-integer quadratic programming (MIQP) problem, and then solved by a distributed method. Simulation studies indicate that the proposed energy trading strategy can reduce the electricity price fluctuation range by 0.43 yuan (20.6%), increase the social cost by 104 yuan (3.5%), and benefit the participators.

Optimal Operation of Energy Hub Based Micro-energy Network with Integration of Renewables and Energy Storages

This study proposes an optimized model of a micro-energy network (MEN) that includes electricity and natural gas with integrated solar, wind, and energy storage systems (ESSs). The proposed model is based on energy hubs (EHs) and it aims to minimize operation costs and greenhouse emissions. The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects. A general algebraic modeling system (GAMS) is used to solve the optimal operation problem in the MEN. The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons. It also yields significant reductions in operation costs and emissions. The proposed model can contribute to future research by providing a more efficient network model (as compared with the traditional electricity supply system) to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.

Electricity Procurement Strategies under Supply Disruption and Price Fluctuation

Improving the reliability of electricity supply is closely related to the national industrial economy and people’s livelihood. When procuring electricity, the large consumer faces the risk of insufficient electricity supply. Such insufficiency may be caused by the supply disruption of the upstream electricity generator and the fluctuation of electricity prices in the electricity pool. We establish an expected cost model for the large consumer and a revenue model for the electricity generator by introducing robustness and opportunity functions to analyse the impact of supply disruption and price fluctuation on both members. Then, we obtain the optimal reliability level and subsidy rate. Our analytical results reveal the following points: (1) During an electricity price fluctuation, the sufficient and necessary condition for the large consumer to expect high robustness of his own decision‐making is to pay a high cost. (2) The high uncertainty of electricity price leads to less revenue for the electricity generator; thus, the electricity generator should perform well in price forecasting. (3) Without disruption risk, the electricity generator is reliable. Furthermore, the electricity pool is unreliable from the perspective of price. However, the electricity supply of the electricity generator is no longer reliable when considering disruption risk, whereas the electricity pool is reliable because the large consumer is tolerant of price fluctuation under this circumstance. Moreover, the impacts of market uncertainty and players’ attributes on the optimal strategies are explored.

An operational bidding framework for aggregated electric vehicles on the electricity spot market

Fluctuating electricity prices offer potential economic savings for the consumption of electricity by flexible assets such as Electric Vehicles (EVs). This study proposes an operational bidding framework that minimizes the charging costs of an EV fleet by submitting an optimized bid to the day-ahead electricity market. The framework consists of a bidding module that determines the most cost-effective bid by considering an electricity price and an EV charging demand forecast module. In this study we develop and evaluate several regression and machine learning models that forecast the electricity price and EV charging demand. Furthermore, we examine the composition of a most optimal operational bidding framework by comparing the outcome of the bidding module when fed with each of the forecast models. This is determined by considering the day-ahead electricity price and imbalance costs due to forecast errors. The study demonstrates that the best performing self-contained forecast models with the objective of electricity price and EV charging demand forecasting, do not deliver the best overall results when included in the bidding framework. Additionally, the results show that the best performing framework obtains a 26% cost savings compared to a reference case where EVs are charged inflexibly. This corresponds to an achieved savings potential of 92%. Consequently, along with the developed bidding framework, these results provide a fundamental basis for effective electricity trading on the day-ahead market.

The Potential of Liquefied Oxygen Storage for Flexible Oxygen-Pressure Swing Adsorption Unit

The pressure swing adsorption (PSA) units are widely used as an oxygen source. Start-up time taking minutes is an undeniable advantage of PSA technology compared to cryogenic air separation start-up time taking hours or days. The increasing share of renewable electricity causes intraday electricity price fluctuations. These fluctuations can be an opportunity to improve the economy of a plant and/or to accumulate electricity in the form of liquefied products. This paper aims to demonstrate the possibility of a flexible PSA unit connected to a small oxy-fuel combustion unit. Two options were analyzed: i) LOX supply at electricity price peak, and ii) liquid oxygen energy storage (LOES). The cold energy needed for oxygen liquefaction will be obtained utilizing liquefied nitrogen (LIN) delivered from a large air separation unit (ASU). The analysis was carried out for the Czech Republic, the Federal Republic of Germany, and the Kingdom of Denmark. These countries differ significantly in the energy mix.

Analyzing the factors influencing the formation of the price of electricity in the deregulated markets of developing countries

The aim of this paper is to measure the impact of certain factors on the formation of the price of electricity, which is obtained from conventional and renewable energy sources, in the deregulated markets of developing countries, such as Montenegro. Another goal is to identify and analyze the criteria that can lead to a change in the electricity market structure. This study was especially inspired by the fact that no similar research has been conducted in this domain, according to the authors’ knowledge. A regression model is applied in the paper, which can be used to display the fluctuation of electricity prices based on the analyzed factors. In addition to price as a dependent variable, the model also examined eight factors that are independent variables. Apart from the basic model, another model has been formulated which, in addition to a price as dependent variable and the examined factors, also takes into consideration the production of electricity from renewable sources. The results of the research showed that an increase in the amount of electricity produced will lead to a slight increase in price, which is to be expected considering there is still one large producer servicing the market, which, despite the deregulation of the market, still has significant monopoly power. The generation of electricity by small hydropower plants will not significantly affect price change, and these entities will behave as if they were in a market of perfect competition. Further opening up the market to new competitors would lead to a fall in the price of electricity. Based on the predicted capacity of electricity generation from small hydropower plants, the electricity deficit could be reduced; in another words it could lead to the reduction of imports. Based on the results of the study, energy policy makers will be able to adopt informed strategies and make decisions related to developments in the energy market, most notably the creation of a common EU energy market. Also, the results of the conducted research expand the theoretical body of work dealing with the determination of the price of electricity. Future studies could address the issue of using natural potential for the generation of electricity from renewable sources, such as solar energy, wind energy and other sources.

Smart Grid Nontechnical Loss Detection Based on Power Gateway Consortium Blockchain

The rapid development of the smart grid brings convenience to human beings. It enables users to know the real-time power supply capacity, the power quality, and the electricity price fluctuation of the grid. However, there are still some threats in the smart grid, which increase all kinds of expenses in the grid and cause great trouble to energy distribution. Among them, the man-made nontechnical loss (NTL) problem is particularly prominent. Recently, there are also some NTL detection programs. However, most of the schemes need huge amounts of supporting data and high labor costs. As a result, the NTL problem has not been well solved. In order to better avoid these risks, problems such as tampering of smart meter energy data, bypassing the smart meter directly connected to the grid, and imbalance between revenue and expenditure of the smart grid are tackled, and the threat scene of NTL is constructed. A hierarchical grid gateway blockchain is proposed and designed, and a new decentralized management MDMS system is constructed. The intelligent contract combined with the elliptic curve encryption technology is used to detect the storage and the acquisition of power data, and the detection of NTL problems is realized. At the same time, it has a certain ability to resist attacks such as replay, monitoring, and tampering. We tested the time consumption and throughput of this method on Hyperledger Fabric. At the same time, eight indexes of other methods proposed in the literature are compared. This method has a good effect.

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

Operation Strategy of Electricity Retailers Based on Energy Storage System to Improve Comprehensive Profitability in China’s Electricity Spot Market

Due to the development of China’s electricity spot market, the peak-shifting operation modes of energy storage devices (ESD) are not able to adapt to real-time fluctuating electricity prices. The settlement mode of the spot market aggravates the negative impact of deviation assessments on the cost of electricity retailers. This article introduces the settlement rules of China’s power spot market. According to the electricity cost settlement process and the assessment methods, this paper proposes a comprehensive electricity cost optimization algorithm that optimizes day-ahead market (DA) electricity cost, real-time market (RT) electricity cost and deviation assessment through ESD control. According to the trial electricity price data of the power trading center in Guangdong province (China), many typical load curves and different deviation assessment policies, the algorithm calculates DA electricity cost, RT electricity cost and deviation assessment cost by utilizing a comprehensive electricity cost optimization algorithm. Compared with the original electricity cost and optimization cost, this method is proven to effectively save overall electricity costs under the spot market settlement system. Based on three different initial investment prices of ESD, this paper analyzes the economics of the ESD system and proves that ESD investment can be recovered within 5 years. Considering the small amounts of operating data in China’s power spot market, the algorithm generates random data according to characteristics of these data. Then, this paper verifies that the comprehensive electricity cost optimization algorithm remains reliable under random circumstances.

A hybrid deep learning-based online energy management scheme for industrial microgrid

The fluctuations in electricity prices and intermittency of renewable energy systems necessitate the adoption of online energy management schemes in industrial microgrids. However, it is challenging to design effective and optimal online rolling horizon energy management strategies that can deliver assured optimality, subject to the uncertainties of volatile electricity prices and stochastic renewable resources. This paper presents an adaptable online energy management scheme for industrial microgrids that minimizes electricity costs while meeting production requirements by repeatedly solving an optimization problem over a moving control window, taking advantage of forecasted future prices and renewable energy profiles implemented by a hybrid deep learning model. The predicted values over the control horizon are assumed to be uncertain, and a multivariate Gaussian distribution is used to handle the variations in electricity prices and renewable resources around their predicted nominal values. Simulation results under different scenarios using real-world data verify the effectiveness of the proposed online energy management scheme, assessed by the corresponding gaps with respect to several selected benchmark strategies and the ideal boundaries of the best and worst known solutions. Furthermore, the robustness of the scheme is verified by considering severe errors in forecasted electricity prices and renewable profiles.

Multi-scenario simulation on the impact of China's electricity bidding policy based on complex networks model

As one of the important measures for China's power system reform, the electricity bidding pricing (EBP) is helpful to realize the effective allocation of power resources. Based on price conduction theory and complex network modeling technology, we construct the price transmission network for the 76 economic sectors in China, identify the critical path of electricity price transmission, and empirically simulate and analyze the economic impact of EBP in different scenarios from the two aspects of whether the CEPL mechanism is implemented or not. The results indicate that the electricity price will be decreased directly by the EBP, and the electricity industry will significantly reduce the impact on other related industries. Particularly, affected by regulatory policies such as electricity price cap, the electricity price caused by EBP cannot be effectively transmitted to the upstream industries. Secondly, with the simultaneous implementation of the coal-electricity price linkage (CEPL) and the EBP, the coal-electricity price transmission will change from one-way conduction style to two-way interaction style, and the impact of electricity price fluctuations on its related industries will be more significant, while the comprehensive impact of the coal industry on its associated industries will be significantly reduced. Thirdly, there are obvious scenario differences in the impact of different intensity of EBP on the macro-economy. The results show that under the mechanism of CEPL, the EBP with 20% of the total power used for bidding pilot has minimal impact on the Chinese economy. Based on the above conclusions, we propose that China should scientifically determine the scale and the pilot regions of electricity bidding under the CEPL, develop more diversified bidding forms and improve more robust supervision system.