Variable Energy Prices Research Articles

Energy poverty in EU: Using regional climatic conditions and incidence of electricity prices to map vulnerability areas across 214 NUTS2 European regions

The rising political instabilities and the associated variations in energy prices have recently amplified the existing debate on energy poverty. Generally defined as the inability to afford enough energy services to live a comfortable life, energy poverty represents a serious issue affecting multidimensional elements of life. To address disparities and to achieve the 7th goal of the 2030 Agenda, EU Member States have been requested to track energy poverty within the National Energy and Climate Plans. The lack of a commonly adopted methodological approach makes however difficult to compare the energy poverty risks. To address this gap, this paper proposes, for the first time, an energy poverty vulnerability indicator that can be used to investigate the differences across NUTS2 European regions. By combining data on regional income, electricity prices and local climatic conditions, the proposed methodology uses a threshold approach to maps vulnerability risks for the years 2010 and 2020. Results show that in 2010 15% of the considered households classified into the highest vulnerability risk and by 2020, 40 additional regions moved to a higher category of risk. Mediterranean countries have been the most affected with extensive climatic and price incidence changes. The proposed indicator can support the definition of policies aiming to reduce regional disparities and improve the average European trends.

The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

A Two-Stage Framework for Exploiting the Multi-Elasticity of Multi-Energy Demands in Integrated Electricity and Gas Markets

With the development of integrated electricity and gas markets (IEGM), the participation of multi-energy demands (MEDs) is an effective measure to achieve a win-win goal of market efficiency improvement and demand cost reduction. The MEDs, composed of terminal users and energy conversion equipment (ECE), can adjust purchased electricity and gas with the variation in energy prices. Such adjustment behaviors of purchased energy in response to energy prices are defined as the multi-elasticity of MEDs. Considering that, this paper aims to analyze and quantify the multi-elasticity of MEDs and exploit it using a market-based approach. Firstly, the volume-price bidding curves are proposed to describe the multi-elasticity of MEDs considering the price-response features of terminal loads and multi-energy substitution features of ECE. Based on the proposed bidding curves, the energy consumption behaviors of MEDs with the change in prices can be effectively quantified. Moreover, a decentralized two-stage market framework is proposed to exploit the multi-elasticity in the IEGM clearing based on the multi-elasticity bidding curves of MEDs. In the proposed framework, the electricity market and gas market are cleared separately with the information exchange of the MEDs' clearing results of purchased gas and electricity. Furthermore, the consensus ADMM-based algorithm is applied to coordinately solve the IEGM clearing problem integrated with the multi-elasticity of MEDs. Numerical simulations are carried out for different test systems to validate the effectiveness of the proposed models.

Representing Hourly Energy Prices in a Large-Scale Monthly Water System Model

Water system management models represent different purposes, such as water supply, flood control, recreation, and hydropower. When building large-scale system models to represent these diverse objectives, their most appropriate time steps for each purpose often do not coincide. A monthly time step is usually sufficient for water supply modeling, but it can be too coarse for flood control, hydropower, and energy operations, where hourly time steps are preferred. Large-scale water management and planning models mostly employ monthly time steps, but using monthly average energy prices underestimates hydropower revenue and overestimates pumping energy cost because these plants tend to operate during times with above- or below-average energy prices within any month. The approach developed here uses hourly varying prices depending on the percent of monthly operating hours. This paper examines an approach that approximately incorporates hourly energy price variations for hydropower and pumping into large-scale monthly time-step water system model operations without affecting water delivery results. Results from including hourly varying energy prices in a large-scale monthly water supply model of California (CALVIN) are presented. CALVIN is a hydroeconomic linear programming optimization model that allocates water to agricultural and urban users with an objective to minimize total scarcity costs, operating costs, and hydropower revenue loss. Thirteen hydropower plants are modeled with hourly varying prices, and their revenue increased by 25 to 58% compared to revenue calculated with monthly average constant energy prices. Hydropower revenue improvements are greater in critically dry years. For pumping plants modeled with hourly varying prices, the energy use cost decreased by 10 to 59%. This study improves system representation and results for large-scale modeling.

Physical Asset Life Cycle Evaluation Models—A Comparative Analysis towards Sustainability

In order to reach a sustainable circular economy, it is important to maximise the life cycle of a Physical Asset. An evaluation of a Physical Asset Life Cycle can be conducted via several approaches, and these may provide different results. The differences may be insignificant, but they must be taken into consideration because they have consequences for a manager’s decisions. This allows for a wider time interval to decide when to withdraw a Physical Asset or renew it and/or if it ought to continue functioning when profits are higher than expenses, thus allowing for a reduction in waste and increase in sustainability. These are some of the aspects that are discussed in this paper; it presents several models for the evaluation of the Physical Asset Life Cycle, considering the market value, devaluation methods and a more generalised use of Fisher’s Equation, which can include the Risk Tax, among others. The results are discussed based on data that support evaluations obtained with the models, and these are used for each Life Cycle model with the aim of evaluating the differences among them. Not only do all of the models consider expenses, namely those in Investment and Functioning, but also profits, which allows for a more holistic evaluation of the Physical Asset Life Cycle. The models are significantly versatile, allowing for a quantitative evaluation of changes in maintenance policies, energy price variations, risks, variations of profits according to the real market and so on. The results demonstrate the robustness of the approach described and indicate that it maximises the Physical Asset Life Cycle, allowing for the consumption of world resources to be minimised and, as a result, contributing to a more sustainable world.

Electric Vehicle Charge Scheduling with Flexible Service Operations

Operators who deploy large fleets of electric vehicles often face a challenging charge scheduling problem. Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off duty at a central depot. Here, high investment cost and grid capacity limit available charging infrastructure such that operators need to schedule charging operations to keep the fleet operational. In this context, flexible service operations, that is, allowing delayed or expedited vehicle departures, can potentially increase charger utilization. Beyond this, jointly scheduling charging and service operations promises operational cost savings through better utilization of time-of-use energy tariffs and carefully crafted charging schedules designed to minimize battery wear. Against this background, we study the resulting joint charging and service operations scheduling problem accounting for battery degradation, nonlinear charging, and time-of-use energy tariffs. We propose an exact branch-and-price algorithm, leveraging a custom branching rule and a primal heuristic to remain efficient during the branch-and-bound phase. Moreover, we develop an exact labeling algorithm for our pricing problem, constituting a resource-constrained shortest path problem that considers variable energy prices and nonlinear charging operations. We benchmark our algorithm in a comprehensive numerical study and show that it can solve problem instances of realistic size with computational times below one hour, thus enabling its application in practice. Additionally, we analyze the benefit of jointly scheduling charging and service operations. We find that our integrated approach lowers the amount of charging infrastructure required by up to 57% besides enabling operational cost savings of up to 5%. Funding: This work was supported by the German Federal Ministry for Economic Affairs and Energy [Grant 01MV21020B]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0272 .

Stochastic multi-benefit planning of mobile energy storage in reconfigurable active distribution systems

This paper proposes a multi-benefit planning framework for mobile energy storage systems (MESSs) in reconfigurable active distribution systems (DSs). The goal of this framework is to improve the DS operation and reliability through achieving four objectives: (1) minimizing the DS costs, (2) minimizing the DS energy losses, (3) improving the DS voltage profiles, and (4) minimizing the expected energy not supplied (EENS) in the system. The developed framework determines optimal decisions associated with the network reconfiguration as well as the MESS sizes and locations, taking into account the variations in DS loads, energy prices, and renewable energy sources. The planning problem in this study is formulated as a stochastic multi-objective mixed-integer nonlinear programming problem, which is solved using a genetic algorithm (GA)-based hybrid optimization method. The proposed framework is tested using a case study of an active DS that includes various types of energy sources and load demands. Comparing the results of the developed planning framework with the base-case system demonstrates the efficacy of the planning framework and the techno-economic benefits of MESSs, where the proposed framework with MESSs successfully achieved significant improvements in the DS operating cost, energy losses, voltage profiles, and EENS. The advantages of MESSs are also verified through a comparison with stationary energy storage systems.

Feasibility of Photovoltaic Systems for the Agrifood Industry in the New Energy and Climate Change Context

The role of the agrifood industry is key to mitigating the impact of climate change, as it is one of the industrial sectors with the highest energy consumption. The optimisation of photovoltaic systems in agroindustries faces problems such as the fluctuation of energy prices or the evident seasonal nature of some producers. This paper provides a global view of the profitability and optimal sizing of photovoltaic (PV) systems in the new energy context. For this purpose, almost 4 million cases were analysed, including different consumption patterns, energy prices, etc. Some general conclusions can be drawn from the results. First, the adaptation to the new context requires adjustments in the sizing of PV systems in all industries analysed, which is also associated with changes in the return on investment. Second, seasonality strongly conditions the optimal size of PV installations, the return on the investment and the potential savings. Finally, in the face of future energy price variations, the ratio “Savings/payback” seems to be an appropriate reference for sizing, combining savings and profitability. In addition, they may justify special subsidies to seasonal industries. The conclusions of this paper should be considered to optimise the design of PVs.

Reinforcement Learning in Power System Control and Optimization

Abstract Reinforcement learning (RL) is area of Machine Learning (ML) and part of wide-range portfolio of the Artificial intelligence (AI) methods. Besides the explanations of the concepts and principles of RL, in the paper are presented practical RL models for control and optimizing operation of power system – controlling tap-changers for maintain voltage levels and model for techno-economical optimizing operation of energy storages of households in microgrid. Trained RL agent in the practical example synchronizes operation of tap-changers to maintain satisfactory voltage level for the consumers, even in the network with distributed generation. Energy storages are in wide use in households, especially in the combination with PV. In the second example, microgrid’s energy management system (EMS) RL agent after learning process act in the simulated environment with variable electrical energy prices, variable load profiles and efficiency of PV modules of households to maximize profit for the houseowners in the microgrid. Agent controls charging and discharging of energy storages and obtain maximal benefit in randomly determined conditions of microgrid operation and different tariff situations. Models are implemented in the Python programming environment Python with specialized power system simulation software (Pandapower) and RL libraries (RLib, OpenAI).

Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios

This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.

Analysis of the Impact of Global Oil Prices On GDP (on the Example of the Azerbaijan Republic)

Purpose of the study. The article analyzes the impact of world oil prices (external and internal factors) on the country’s GDP, considers fluctuations in world oil prices, their impact on the national economy of Azerbaijan and the integrability of these macroeconomic indexes. Materials and methods. The study of the dynamics of the functioning of time series based on the initial data revealed their non-stationarity, which does not allow creating a “qualitative” predictive model. In order to achieve the goals of the study and “improve the quality” of the model being formed, which is used to calculate predictive estimates, appropriate econometric procedures were carried out and the integrability of time series was investigated. In particular, the method of vector error correction model VECM is used. The test is based on the use of cointegration equations between variables, where lag lengths and Granger causality definitions are solved within this model. When forming the VECM model, the hypotheses put forward in the work were tested using econometric tests. The responses of the impulse function to the independent variables of the model were studied by the method of graphical representation based on the values of the model and its residuals.Results. It has been determined that the long-term equilibrium relationship between variables can be considered stable, since after short-term disturbances from shock reactions, stability is restored. The applied method of decomposition of forecast error variances to determine the influence of exogenous variables on the endogenous variable showed that the greatest uncertainty in the forecast for GDP, Azeri_light, Brent and West is given by their own changes during the first trimester of the period under consideration.Conclusion. The results obtained can be useful for identifying real trends in Azerbaijan’s GDP and determining its interdependencies with other macroeconomic variables, for determining its interdependencies with variations in energy prices based on an analysis of the dynamics of the indexes under consideration, for developing recommendations and forming directions for the longterm development of GDP.

A novel robust exact decomposition algorithm for berth and quay crane allocation and scheduling problem considering uncertainty and energy efficiency

In container terminals, electricity price variation has a tangible impact on the operating cost of port equipment, especially the quay crane (QC). Accordingly, significant operational cost savings could be realized via careful planning. This study addresses, for the first time, berth allocation and QC assignment and scheduling problem (BACASP) considering energy price variations. A novel mathematical model is developed to minimize both energy costs and vessel tardiness. Furthermore, we consider the uncertainty in QC processing time and vessel arrival time, based on which we propose a robust formulation that optimizes the worst-case scenario. This study also highlights why robustness is essential for cost-cutting considering the energy prices in the BACASP. The model is unable to achieve optimality using CPLEX, even for small instances. Thus, a novel exact decomposition algorithm is proposed to solve the problem over a reasonable computational time. Its novelty lies in four proposed strengthening procedures designed and embedded within the algorithm. To test the algorithm, several instances are generated based on the literature and real data of a port partner. Then, several experiments are conducted to highlight the impact of implementing the four strengthening procedures and demonstrate how considering energy constraints within the BACASP leads to significant cost savings.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Investigating the impact of variable energy prices and renewable generation on the annualized cost of hydrogen

Hydrogen can be produced from onsite renewable energy, energy from the power grid, or a combination of both. While the cost of grid energy is driven by the energy prices, onsite renewable investments are driven by the levelized cost of renewable energy. Energy prices can widely vary in time and in different locations, potentially having a large impact on the annualized cost of hydrogen (ACOH). Renewable sources can also vary significantly for different locations and seasons. In this paper, we develop a semi-empirical model to find key ACOH locational drivers and trends. The model finds the optimal capacity of the onsite solar and wind generators and the electrolyzer, together with the optimal use of grid energy. We found that an optimal balance between onsite renewable investments and grid energy use is strongly driven by the type of energy price (fixed or variable) and location.

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers string-matching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC. The primary goal of this study is to use a string-matching algorithm (i.e., Boyer Moore) to compare the contents of incoming workloads to those of documents that have already been processed in a data center. A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy. On the contrary, if no match can be discovered, the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices, the number of active servers, on-site green energy, and traces of incoming workload. The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

Electric Vehicle Charge Planning by Deep Reinforcement Learning

In this paper, we study the problem of planning the charging of an electric vehicle (EV) for long distance trips. In particular, a dedicated tool is needed in order to suggest the driver the best traveling and charging plan based on her/his preferences and on the available charge infrastructures along the route. An ad-hoc tool is developed using Matlab and Simulink software in order to model the EV energy charging/discharging, and to optimize the trip plan based on three alternative goals: minimizing the travel time; minimizing the charging cost; optimizing travel time and cost. The proposed model is based on the use of Deep Reinforcement Learning (DRL) algorithm in which an agent is rewarded or penalized while learning the best charging options along the route according to the three above goals. In particular, in a dynamic energy market context, the model is designed to be robust at energy price variations and at cruise speed variations as well. In addition, a dashboard is designed and developed to provide an user-friendly interface to set the EV and trip parameters and to monitor the trip planning results. A case study demonstrates the application of the proposed tool.

Energy, economic and comfort optimization of building retrofits considering climate change: A simulation-based NSGA-III approach

Improving building energy performance is central to promote sustainability and mitigate climate change, but it has proved to be a challenging process requiring investigation of a large domain of energy retrofit measures and contrasting objectives. Besides, the unique characteristic of Iran, namely heavily subsidized energy prices and tiered utility rates, further complicate the problem. This research introduces a novel simulation-based multi-objective optimization approach based on a modification of NSGA-III algorithm, denoted as prNSGA-III, powered by parallel computing design and result-saving archive that offers an increased computational potential. The method is developed by integrating EnergyPlus as energy simulation engine with optimization algorithm coded in MATLAB programming language. A large domain of active, passive, water conservation, and renewable retrofit measures are explored to maximize environmental performance while minimizing thermal discomfort and life cycle cost. The method is applied to a residential building in Tehran, Iran, subjected to different scenarios with reference to future climate change and energy price variation. The results show that implementing suggested energy retrofit measures can yield substantial improvement in environmental performance and curb CO-eq emission by up to 73%, and also reduce thermal discomfort by up to 46%. However, energy-efficient solutions can only be achieved at the expense of increased life cycle cost. Moreover, the result demonstrates that climate change can affect optimal solutions, particularly heating and cooling systems, due to changes in external thermal loads.

Commercial consumers pay attention to marginal prices or average prices? Implications for energy conservation policies

Any energy conservation policy intervention associated with energy price variation needs to consider whether consumers respond to marginal or average pricing. To the best of our knowledge, there is no such evidence for commercial electricity consumers (such as office buildings and malls), although commercial consumers are responsible for nearly 18% of electricity consumption in the United States. This study examines a four-tiered decreasing-block pricing schedule for commercial consumers. Based on individual-consumer-level data, we analyze the daily electricity consumption of 597 commercial accounts in Phoenix metropolitan, Arizona, from May 1, 2013, to December 31, 2016. We run 2SLS models with policy-induced price variation as instrumental variables to estimate the effects of marginal and average prices on commercial electricity consumption at each cutoff point. We also study the heterogenous response across industry sectors. Our analysis shows that commercial consumers respond to both marginal and average prices, but have different responses with respect to how much electricity they consume. Higher-usage consumers tend to respond more to average prices, whereas lower-usage consumers are more sensitive to marginal prices. Our findings indicate that conservation policies should be tailored differently for commercial electricity consumers. Nonlinear electricity pricing structures can reduce energy consumption, particularly for commercial consumers who have lower energy demand if the pricing structure shifts from decreasing to increasing blocks. In contrast, a flat rate with a higher price level can limit the electricity consumption of high-use consumers. As for an industry-wise policy design, nonlinear pricing can be effectively used to reduce aggregate consumption in the construction, manufacturing, real estate, and rental and leasing industries, professional, scientific, and technical services, other services (except public administration), and public administration industries.

Demonstration of non-linear model predictive control for optimal flexible operation of a CO2 capture plant

Due to the penetration of renewable intermittent energy, there is a need for coal and natural gas power plants to operate flexibly with variable load. This has resulted in an increasing interest in flexible and operational issues in the capture plant as well. In the present paper a nonlinear model predictive control (NMPC) system was tested at the Tiller pilot plant in Norway. The most important part of the NMPC software is the dynamic model representing the absorber/desorber plant. A previous first principle (mechanistic) dynamic model of the plant using MEA was modified for a solvent of AMP and piperazine, and then successfully verified by step response tests. The NMPC, which was set up to minimize the deviation from the capture rate setpoint and minimize the specific reboiler duty was then tested in a closed loop with large changes in flue gas flow and CO2 composition. Even for gas rate variations of more than 300% (110–340 m3/h) and CO2 concentration changes of 30%, the dynamic response was satisfactory. A test with frequently occurring constraints on the reboiler duty revealed a need for an extension to include direct control of the lean loading. Test of setpoint changes in total CO2 recovery showed that the control system managed to rapidly change from one capture rate to another with a time constant of typically 10 min. This might be used in a second layer of optimization, a dynamic real-time optimizer, that minimizes the capture costs during a longer horizon considering varying energy prices.

Equilibrium sorting and moral hazard in residential energy contracts

This paper studies tenant behavior in rental housing when the landlord pays for heating. I develop a model in which renters have heterogeneous preferences for home size and indoor temperature. When energy is costly, renters choose smaller apartments and turn down the heat—or sort into apartments with landlord-pay energy bills. I estimate the model using exogenous variation in energy prices and use a machine-learning algorithm to explore preference heterogeneity. Surprisingly, I find that renters who prefer hotter temperatures do not systematically choose landlord-pay units, though I am unable to rule out sorting on preferences for unobserved home attributes. Eliminating moral hazard by forcing all renters to pay their own bill reduces energy consumption by 25% due to renters turning down the heat (21%) and choosing smaller units (4%). Moral hazard in residential energy contracts costs the United States $836 million per year in welfare losses including $246 million from carbon emissions.