Fluctuating Electricity Prices Research Articles

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

The existing research on critical peak price (CPP) decision-making ignores the difference in risk appetite between industries within the consumer population, resulting in a serious lag in the enthusiasm of some users to respond to CPP, and unsatisfactory improvement of power systems and carbon emission reduction on the supply and demand side. Firstly, the problem of consumer risk appetite was comprehensively analyzed, and the industrial consumer population was secondarily stratified according to the influencing factors and the enthusiasm of responding to CPP, namely: stubborn, active and conformist, and quantitatively verified by cluster analysis. Secondly, by combing the relevant paths of CPP decision-making, the critical-peak window determination model and CPP multi-objective optimization model were constructed, and the calculation of relevant indicators was introduced. Finally, taking 10 industrial enterprises in a city in Sichuan Province as an example, the clustering method was used to verify the stratification results, and the index analysis method was used to measure the load and carbon emission improvement of two typical enterprises after CPP optimization. The results showed that the stubborn users insist on using electricity, the improvement effect of load and carbon emission reduction was poor, and additional production costs will be caused. The conformist and active users had high sensitivity to electricity price fluctuations, good load and carbon emission reduction improvement effects, and significantly reduced electricity costs.

A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources

As the penetration rate of variable renewable energy such as wind power increases in the power system, the composition and balance of the system also change gradually. The intermittency of renewable energy poses great stability challenges to the traditional centralized generation and load-oriented transmission and distribution methods. Therefore, the Active Distribution Network Operator (ADNO) with distributed installation at the local level has a good application prospect in the new scenario. However, ADNO needs to improve its operational efficiency based on the types of local generation and storage devices and the nature of the market environment. To address this issue, this paper proposes a forecasting method that considers the coupling fluctuations of spot electricity prices and renewable energy, and a bi-level optimization operation method based on the Stackelberg game for optimizing the operation of small-scale ADNO under high wind power penetration rate. Simulation results show that the proposed methods achieve greater positive impact on the operational efficiency of ADNO than conventional methods. In addition, the proposed methods ensure the long-term profitability of ADNO, even with fluctuations in external factors.

Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model

In recent years, frequent linkage events of natural gas and electricity prices have seriously affected the stability of the energy supply market. In view of the increasingly close linkage relationship between gas and electricity prices, a gas–electricity price linkage analysis method considering the benefit–cost and price transmission of gas units is proposed. Firstly, a benefit–cost model of gas units based on risk premium theory is constructed to reflect the willingness of gas units to offer prices in different market environments. Secondly, a vector error correction model (VECM)-based gas–electricity price conduction analysis method was proposed to study the long-term co-integration relationship between gas–electricity prices and the short-term fluctuation conduction process. Then, aiming at the problem of delay in price transmission, a time-delay analysis method of gas–electricity price based on the attention mechanism is proposed. Finally, the gas price index and average gas power price data of China from August 2020 to November 2022 are used to verify the validity of the proposed model; the numerical results show that the proposed method can determine the long-term stable fitting relationship between gas and electricity prices, and can also analyze and judge the conduction direction of gas and electricity price fluctuations in different time periods.

Demand–Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization

Electric Storage Water Heaters (ESWH) are a widespread solution to supply domestic hot water (DHW) to dwellings and other applications. The working principle of these units makes them a great resource for peak shaving, which is particularly important due to the level of penetration renewable energies are achieving and their intermittent nature. Renewable energy deployment in the electricity market translates into large electricity price fluctuations throughout the day for individual users. The purpose of this study was to find a demand–response strategy for the activation of the heating element based on a multiobjective minimization of electricity cost and user discomfort, assuming a known DHW consumption profile. An experimentally validated numerical model was used to perform an evaluation of the potential savings with the demand–response optimized strategy compared to a thermostat-based approach. Results showed that cost savings of approximately 12% can be achieved on a yearly basis, while even improving user thermal comfort. Moreover, increasing the ESWH volume would allow (i) more aggressive demand–response strategies in terms of cost savings, and (ii) higher level of uncertainty in the DHW consumption profile, without detriment to discomfort.

Electricity market margin determination method considering the integrity of electricity assets

China is pushing ahead with the development of the electricity market based on renewable energies. Electricity futures and options are an important trading instrument in the electricity market, and margins in the electricity market are often used to measure the value at risk of derivatives such as options on electricity futures, which are usually calculated based on the SPAN system. However, traditional SPAN systems do not take into account the integrity of electricity (IoE) derivatives, which would affect the risk valuing and capital usage efficiency. This paper proposed an improved margin calculation method that considers the IoE and gave an example for explanation. Compared with the previous calculating method, the method can effectively control the sharp fluctuations in electricity prices when renewable energy sources are traded in the electricity market, and effectively control the default risk in the market.

Demand response model by locational marginal electricity–carbon price considering wind power uncertainty and energy storage systems

With the increasing prominence of global carbon emissions, how to achieve carbon emission reduction in power systems is an urgent issue. Early research introduced carbon cost in the objective functions or constraints in an optimization model. However, the carbon cost cannot account for the time-varying carbon emissions caused by load electricity consumption. To address this challenge, this paper proposes a demand response (DR) program based on the locational marginal prices on both electricity consumption and carbon emission to reshape the load consumption pattern, which guide the load from both “electricity perspective” and “carbon perspective”. This model can reduce the carbon emissions of the power system while considering the economics of power purchase and the traditional DR to peak load shifting. Meanwhile, historical data fitting, Monte Carlo sampling and scenario reduction techniques are used to take full account of wind power uncertainties. The energy storage (ES) systems are introduced to simultaneously arbitrage from both electricity and carbon markets by appropriate charging strategies. The simulation results using PJM 5-bus system show that the locational marginal electricity–carbon price proposed in this paper can reduce the carbon emissions of the power system while ensuring an economic operation. By adding the carbon emission factor to the price, the load demand not only follows the fluctuation of electricity price, but also shows the trend of following the change of carbon emission.

Providing a mathematical model to select governmental supporting scenarios for renewable energy plants

ABSTRACT In this study an extended model is proposed based on government subsidies to support the development of new energy power plants. Accordingly, the profit of this production unit is increased with the reduction of CO2 emissions. In addition, the results show that the plan to sell carbon dioxide emissions threshold helps in financial subsidies saving. Also, the increase in factors such as energy production capacity, electricity market price, CO2 emission threshold price and fluctuations in investment costs can lessen the amount of dedicated subsidies by the government. Also, the increase in parameters such as investment costs and fluctuations in electricity prices, and the price of CO2 emission thresholds leads to the dedication of more encouragement subsides.

Energy storage systems with distributed generation in power network reconfiguration using improved artificial bee colony algorithm

Recently, the Distributed Generation (DG) structure has become an important aspect of daily planning and has achieved a variety of functions while maximizing the functionality of utilities. It should balance the energy equilibrium of the system components and the production limit. Uncertainties arise during the operation of the DG, represented by generator power, load requirements and fluctuations in electricity prices. The reactive power problem is the major concern associated with the DG system. This paper analyzes both DG and an energy storage system along with their optimal performance using a specific algorithm. For optimal analysis of DG and ESS, an Improved Artificial Bee Colony Algorithm (IABC) is proposed. The IABC evaluates the performance of distribution network. It takes DG and ESS into account for analyzing optimal design problems, the main goal of which is to minimize their reactive power dispersion functions. In addition, there are barriers to reducing the DG’s operating costs. The proposed method is implemented on the MATLAB platform and tested using the IEEE 33-bus system. To confirm the effectiveness of the proposed method, it will be compared with the existing methods such as artificial bee colony (ABC) and levy-based method, respectively.

Trading Portfolio Strategy Optimization via Mean-Variance Model Considering Multiple Energy Derivatives

Energy retailers that sell energy at fixed prices are at risk of bankruptcy due to instantaneous fluctuations in wholesale electricity prices. Energy derivatives, e.g., electricity options, can be purchased by energy retailers then sold to customers as one potential risk-mitigation tool. A class of energy retailers that trade energy derivatives, including the electricity option, the carbon option and the green certificate, is considered in this paper. In terms of energy retailers, a strategy that can maximize the value of the purchased energy derivatives over a period of time and minimize the risk due to the stochastic price fluctuations is developed. Firstly, the dynamic prices of the electricity option as well as the carbon option are described by stochastic differential equations, and the dynamic prices of the green certificate are described by ordinary differential equations. Historical price data are used to obtain the parameters of both stochastic and ordinary differential equations by maximum likelihood estimation. Next, an investment portfolio is established as a mean-variance portfolio selection problem where the retailer maintains the satisfactory asset value and minimizes the risk simultaneously. Then, the problem is transformed into a stochastic optimal control problem which can be solved analytically by using the linear-quadratic method. Finally, the numerical simulations illustrate the feasibility of the proposed method.

Observable placement of phasor measurement units for defense against data integrity attacks in real time power markets

Integrity data (DI) attacks are considered malicious cyber threats to the economic performance of power markets in current power systems. A cyber attacker could mislead the system operator by implementing a DI attack, through the deviation of measured information, and causes non-optimal power distribution and erroneous participation in the electricity market (EM). This paper proposes a placement scheme of phasor measurement units (PMUs) to defend against these attacks, so that network observability is guaranteed; the possibility of detecting DI attacks by the operator is increased; and the effect of electricity price fluctuations caused by these attacks is prevented. For this purpose, we introduce two possible indices to determine the degree of attack detectability and the magnitude of system congestion variation. Accordingly, the two-objective placement model of PMUs is upgraded, in which the minimum number of PMUs and their placement must be specified to improve the proposed indices so as to minimize the possibility of financial misconduct taking place in the real time market. Using IEEE standard systems, the effectiveness of this PMU placement-based defense scheme has been confirmed.

Optimized Energy-Flexible Operation Strategies of Air Conditioning Systems in Production Environments

Rising energy prices and regulatory requirements are forcing both large manufacturing companies and small and medium-sized enterprises to minimize costs and emissions associated with energy supply. To operate the considered systems more efficiently overall, numerous energy efficiency measures have already been successfully implemented in recent years. This is particularly the case for the cross-industry air conditioning systems that are the focus of this paper. However, due to the growing share of renewable energies and the resulting fluctuating electricity prices and CO2 emissions factors, energy flexibility measures are becoming increasingly relevant. In this context, flexibility measures allow industrial system operators to benefit from these fluctuations, primarily using energy storages and optimized operation strategies. However, the implementation of energy flexibility measures requires high data availability, a good understanding of the system, and flexibility in production planning. To be able to effectively use the existing energy flexibility potentials, air conditioning systems are examined, that contain widely used cross-section technologies. In this paper, a method to enable optimized energy-flexible operation of industrial air conditioning systems is presented. A common feature of manufacturing sites is a similar topology of the thermal supply infrastructure across industries. The complex overall system is divided into several subsystems, e.g., cooling supply system, ventilation system and the production hall. Production processes typically release waste heat into the indoor air of the production hall, which then must be cooled. By accounting for thermal and electrical output and optimizing the operation strategy using mathematical optimization, both operating costs and CO2 emissions associated with energy provision can be significantly reduced. The presented use case reveals that operational cost savings of up to 18 % and a reduction of CO2 emission associated with energy supply of up to 16 % can be realized by optimizing the cooling supply system on its own.

Techno-economic assessment of green ammonia production with different wind and solar potentials

This paper focuses on developing a fast-solving open-source model for dynamic power-to-X plant techno-economic analysis and analysing the method bias that occurs when using other state-of-the-art power-to-X cost calculation methods. The model is a least-cost optimisation of investments and operation-costs, taking as input techno-economic data, varying power profiles and hourly grid prices. The fuel analysed is ammonia synthesised from electrolytic hydrogen produced with electricity from photovoltaics, wind turbines or the grid. Various weather profiles and electrolyser technologies are compared. The calculated costs are compared with those derived using methods and assumptions prevailing in most literature. Optimisation results show that a semi-islanded set-up is the cheapest option and can reduce the costs up to 23% compared to off-grid systems but leads to e-fuels GHG emissions similar to fossil fuels with today’s electricity blend. For off-grid systems, estimating costs using solar or wind levelized cost of electricity and capacity factors to derive operating hours leads to costs overestimation up to 30%. The cheapest off-grid configuration reaches production costs of 842 €/tNH3. For comparison, the ”grey” ammonia price was 250 €/tNH3 in January 2021 and 1500 €/tNH3 in April 2022 (Western Europe). The optimal power mix is found to always include photovoltaic with 1-axis tracking and sometimes different types of onshore wind turbines at the same site. For systems fully grid connected, approximating a highly fluctuating electricity price by a yearly average and assuming a constant operation leads to a small cost (<4%) overestimation compared to the more advanced optimisation method applied here .

Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market

The structure of the electricity market in Iran is based on a pay-as-bid auction mechanism. In such a market, hydropower generators need to have accurate estimates of energy price in peak hours of the day-ahead market to optimally operate the reservoir and maximize the revenue. This paper aims at providing a robust model with the best predictors for forecasting the maximum daily electricity price (MDEP) in Iran's electricity market. To reach the goal, hourly electricity prices in 2020 and 2021 were used and several artificial intelligence models were employed to predict the MDEP and ADEP (average daily electricity price). A sensitivity analysis of the inputs showed that in most of the models for forecasting the day-ahead MDEP (Ptmax), the best predictors were Pt−1max,Pt−7max,andPt−30max. Also, the convolutional neural-long short-term memory network (CNN-LSTM) had the best performance for forecasting both MDEP and ADEP in Iran's energy market. Compared to the multivariate linear regression model, the CNN-LSTM dealt well with sinusoidal characteristics and fluctuation of electricity prices. Therefore, it could improve the accuracy and correlation of the MDEP forecasts by 31% and 3.5%, respectively.

Modeling and simulation of smart grid-aware edge computing federations

Compute-intensive Internet of Things (IoTs) applications have led to the edge computing paradigm. Edge computing decentralizes the IT infrastructure in multiple edge data centers (EDCs) across the access networks to reduce latency and network congestion. Edge computing can benefit significantly from different aspects of smart grids to achieve lower energy consumption and greater resilience to electricity price fluctuations. This paper presents a modeling, simulation, and optimization (M&S&O) framework for analyzing and dimensioning smart grid-aware edge computing federations. This tool integrates aspects of a consumer-centric smart grid model to the resource management policies of the EDCs. To illustrate the benefits of this tool, we show a realistic case study for optimizing the energy consumption and operational expenses of an edge computing federation that provides service to a driver assistance IoT application. Results show that this approach can reduce the daily energy consumption by 20.3% and the electricity budget by 30.3%.

Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework

This paper develops a two-stage multi-objective bi-level framework to optimize the sizing of a grid-connected electricity-hydrogen system. Firstly, a multi-objective bi-level capacity configuration optimization model considering the different functional orientations of hydrogen energy and electricity-price prediction is established. Then, to solve the above multi-objective bi-level model, a two-stage solution algorithm is proposed. In stage one, the CPLEX solver and non-dominated sorting genetic algorithm II are employed to obtain the solutions of the developed optimization model. In stage two, an entropy method is applied to get the importance of the three objectives of the outer model, whereas a cumulative prospect theory is used to rank the best Pareto solution. Finally, an industrial park in Aksai Kazak Autonomous County is chosen for case study, the results show: (1) the best capacity configuration alternative, which includes 22 wind turbines, 210 photovoltaic panels, 2 gas turbines, 2 fuel cells, 1 electrolyzer, and 3 hydrogen tanks, owns the NPB of 161,503 CNY, the ACE of 93,111 kg, and the LOEC of 603,874 kWh. (2) the ACE with the weight of 0.527 is the most important objective. (3) Sensitivity analysis on electricity price fluctuations of ±5% and ±10% presents that the proposed approach is robust.

Optimal design and scheduling of carbon capture power plant based on uncertainty decision-making methods

Post-combustion capture is one of the most mature carbon reduction technologies which plays an important role in pushing forward the process of "carbon neutrality". However, most previous efforts focused merely on the fixed operation of power plants and carbon capture devices, without taking into account the effects of fluctuating electricity prices and the subsequent variations in energy penalty of solvent regeneration process. As a result, the ability of carbon capture power plants (CCPP) to withstand fluctuations has deteriorated, leading to a decrease in robustness. In order to take advantage of the flexible operation characteristics of the power plant and the carbon capture device, the deterministic hourly electricity price and power load demand, as well as uncertainty of power load demand in future are considered in the study to determine the optimal network configuration and the flexible scheduling of carbon capture power plants based on decision-making theories. The uncertain parameters are presented by scenarios, and through MINLP-based optimization, the optimal system configuration under each scenario is obtained. Then these configurations are applied to other scenarios to obtain the scheduling and economic performance of the system. Five uncertainty decision-making theories (optimistic method, pessimistic method, optimistic coefficient method, equal probability method, minimum maximum regret value method) are adopted to select the most appropriate system scheme. Finally, a case study is investigated to demonstrate the proposed method, and the effectiveness and the feasibility are proved by the obtained results. Two network configurations with daily profit from $30,000 to $240,000 by scenarios are obtained and investigated. Results show that the turbine layout in the configuration chosen by optimistic method is radical, while by pessimistic criterion the turbine system shows margin in operation. Configuration selected by the equal possible value method and minimax regret method can achieve good economic performance operating in various scenarios. This research can help decision makers to make scientific decisions and reduce operating risks under uncertainties.

Optimal scheduling management of the parking lot and decentralized charging of electric vehicles based on Mean Field Game

As an intermediary for the interaction between the grid and electric vehicles (EVs), the parking lot aggregator not only facilitates the exchange of electricity between EVs and the grid, but also brings benefits to all participants. This paper proposes a linear quadratic (LQ) Mean Field Game (MFG) theory with a major player to optimal scheduling management of the parking lot and formulate optimal decentralized charging control strategies for a large number of EVs, to achieve the minimization of EVs charging cost while maximizing the profit of the parking lot, but these two problems are a set of coupled control problems. In addition to modeling the interaction between the parking lot and the EVs as a finite-time dynamic game problem, the Nash Certainty Equivalence (NCE) of related optimization problems is also proposed, and the corresponding solution algorithm is designed. The control effects of the proposed dynamic game problem on the charging cycle, as well as the effects of parameters change and electricity price fluctuations on charging control are illustrated through numerical simulations.

A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis

Precisely identifying multidimensional trends and hidden characteristics that relate to short-term electricity price fluctuations and providing reliable predictions of future trends are difficult tasks. Instead of utilizing conventional regression-based forecasting methods, we present a novel methodology to address the problem of obtaining reliable forecasts from a pattern classification standpoint. Given that an attention mechanism is better able to capture global characteristics, a multi-head self-attention (MHSA) mechanism is adopted to extract features at long time scales more efficiently and ensure that long-term dependencies can be captured. On this basis, a new hybrid framework composed of nested long short-term memory (NLSTM), the MHSA mechanism combined with a convolutional neural network (MHSAC), and a feature space identification approach is established for robust interval forecasts. To verify the performance of our framework and demonstrate its application potential, the proposed classifier is compared to benchmarks under various scenarios (8 different input dimensions and 25 different input sizes) in terms of different performance criteria. The results indicate that our framework can be used as a valid alternative for electricity price forecasting, and it achieves satisfactory forecasting accuracy with different input dimensions. Furthermore, compared with regression-based models and price spike forecasting cases, our framework is more effective than the benchmarks. The findings also suggest that appropriate feature selection is more conducive to improving model forecasting accuracy than blindly increasing the dimensions of the input data, and the proposed framework that incorporates the MHSA mechanism is also propitious for further improving the efficiency of electricity price forecasting.

A two-stage distributionally robust optimization model for optimizing water-hydrogen complementary operation under multiple uncertainties

Under the pressures of fossil energy depletion and the “Carbon peak and neutrality” target, the development of clean energies such as hydropower and hydrogen has received widespread attention. The integration of hydropower, power-to-hydrogen/hydrogen-to-power and energy storage (forming a water-hydrogen complementary system) can improve the water resource utilization and obtain additional benefits by selling hydrogen etc. However, random fluctuations in market electricity prices, water flow and electric load seriously interfere with the complementarity of water and hydrogen, hindering the acquisition of the above benefits. To this end, this paper proposes a two-stage distributionally robust optimization model to solve the operation scheduling issue of the water-hydrogen complementary system under multiple uncertainties. Specifically, the uncertain distribution of market electricity prices, water flow and electric load forecasting errors are depicted with a moment-based ambiguity set. In the first stage, electricity and hydrogen are coordinately scheduled based on the forecast information to maximize the operation profit of the complementary system. In the second stage, the operations of flexibility resources are linearly adjusted from the first stage to resist the interference of the “worst-case” distribution in the ambiguity set. Finally, the model is equivalently reformulated into a mixed integer linear programming for solution feasibility. Simulation verifies that: 1) the model is conducive to the complementary system operation, such as 43.7% profit improvement (compared with scheduling ignoring uncertainties), 97.70% water utilization and effectively resisting uncertainties; 2) the model keeps low conservativeness and computational complexity compared with the stochastic and robust optimizations.

A master–slave game optimal scheduling strategy for park-integrated energy systems based on adjustable robust optimization

As the bridge between power companies and users, the integrated energy system has become one of the carriers of energy reform, energy-saving, and emission reduction. Based on this, a master–slave game bilevel optimization model considering power company–park-integrated energy system (PIES)–user is established. In the upper game, the power company, as the leader, takes the maximization of its interests as the goal to consider and formulate the price of purchasing and selling electricity with the park. As a follower, combined with the fluctuation of electricity price and the electricity demand of its equipment, the park determines the relationship between purchasing and selling electricity with the power company. In the lower-level game, the park becomes the leader, taking into account the energy needs of users and formulating a reasonable price for selling energy. Users, as followers, intend to maximize consumer surplus and adjust their energy demand strategies to achieve the best energy consumption experience. Analyzing the properties of the game, it is verified that there is a unique Nash equilibrium solution in the game model. At the same time, the idea of solving the distribution of the model is adopted, and the equilibrium solution of the model is obtained by using limited information. In addition, the output uncertainty of renewable energy in the park is dealt with by adjustable robust optimization. Finally, aiming at achieving a win–win situation among all stakeholders, the proposed game model is verified to effectively solve the equilibrium strategy problem among the PIES, the power company, and users through simulation analysis of an example.