Time-varying Electricity Prices Research Articles

Energy consumption optimization of chiller plants with the genetic algorithm based GWO and JAYA algorithm in the dynamic pricing demand response

Demand response is a program in the electrical system that is used to increase or decrease the energy demand of the consumers where the consumers can actively respond to the system requirements. We developed an energy scheduling system to schedule the consumer load in a dynamic pricing scheme to reduce the cost and lower the peak demand. The problem formulation is done to model the food storage chiller plant for cost minimization with the consumer constraint. The optimal schedule is to be generated in a real time varying electricity price scheme. To solve this optimization problem Grey wolf optimizer (GWO) algorithm and JAYA optimization algorithms are used. Besides, the Genetic algorithm (GA) based GWO and GA based JAYA algorithm are developed and simulated. The genetic algorithm improves the population diversity of the solutions. The simulation results show a 22 % cost reduction and 10 % energy consumption reduction as compared with normal control method and consumer constraints are satisfied. The algorithms GA-GWO and GA-JAYA give better results for optimal cost minimization in terms of the reduced cost, energy consumption, energy consumption shift to low electricity price hours and temperature maintained below the mean set temperature through the entire duration.

An electric vehicle routing model with charging stations consideration for sustainable logistics

PurposeRecently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.Design/methodology/approachThis study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.FindingsThe result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.Research limitations/implicationsThe optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.Originality/valueIn prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Optimal Control of District Cooling Energy Plant With Reinforcement Learning and MPC

Abstract We consider the problem of optimal control of district cooling energy plants (DCEPs) consisting of multiple chillers, a cooling tower, and a thermal energy storage (TES), in the presence of time-varying electricity price. A straightforward application of model predictive control (MPC) requires solving a challenging mixed-integer nonlinear program (MINLP) because of the on/off of chillers and the complexity of the DCEP model. Reinforcement learning (RL) is an attractive alternative since its real-time control computation is much simpler. But designing an RL controller is challenging due to myriad design choices and computationally intensive training. In this paper, we propose an RL controller and an MPC controller for minimizing the electricity cost of a DCEP and compare them via simulations. The two controllers are designed to be comparable in terms of objective and information requirements. The RL controller uses a novel Q-learning algorithm that is based on least-squares policy iteration. We describe the design choices for the RL controller, including the choice of state space and basis functions, that are found to be effective. The proposed MPC controller does not need a mixed integer solver for implementation, but only a nonlinear program (NLP) solver. A rule-based baseline controller is also proposed to aid in comparison. Simulation results show that the proposed RL and MPC controllers achieve similar savings over the baseline controller, about 17%.

Can Locational Disparity of Prosumer Energy Optimization Due to Inverter Rules Be Limited?

To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measurements to ensure that inverter output does not cause voltage violations. However, such actions, as observed in this work, restrict the range available for local energy management, with more adverse losses on arbitrage profits for prosumers located farther away from the substation. The goal of the paper is three-fold: (a) to develop an optimal local energy optimization algorithm for activation of load flexibility and inverter-interfaced solar PV and energy storage under time-varying electricity prices; (b) to quantify the locational impact on prosumer arbitrage gains due to inverter injection rules prevalent in different energy markets; (c) to propose a computationally efficient hybrid inverter control policy which provides voltage regulation while substantially reducing locational disparity. Using numerical simulations on three identical prosumers located at different parts of a radial feeder, we show that our control policy is able to minimize locational disparity in arbitrage gains between customers at the beginning and end of the feeder to 1.4%, while PV curtailment is reduced by 91.7% compared to the base case with restrictive volt-Var and volt-watt policy.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

Combining predictive and prescriptive techniques for optimizing electric vehicle fleet charging

The last decade has witnessed a burgeoning interest in transportation electrification from the academia, government, and industry. A current barrier faced by fleet operators is the charge scheduling, a problem that becomes more pronounced with the fleet size, heterogeneity (in both the vehicle fleet and the charging infrastructure), and uncertainty, which has given rise to the Charging-as-a-Service (CaaS) industry. A CaaS provider intermediates between the fleet owner and the macrogrid, and is key to ease the transition to the future of transportation with electric vehicles. This paper addresses the CaaS providers’ electric vehicle fleet (EVF) charge scheduling problem with time-varying electricity prices. We develop a rolling-horizon online optimization approach reinforced with a predictive model and a heuristic warm-start to solve this emerging multi-stage stochastic optimization problem. A numerical experiment demonstrates that our method outperforms an industry benchmark by 13.24%–18.44% with respect to charging costs under the tested conditions. In addition to cost savings, the energy use profile of resulting schedules consumes less energy in peak hours, which can reduce carbon emissions and improve grid stability. Thus, the proposed approach identifies charging schedules that simultaneously benefit CaaS providers, fleet owners, electric power producers, and the macrogrid in general.

Survey- and simulation-based analysis of residential demand response: Appliance use behavior, electricity tariffs, home energy management systems

Residential demand response (DR) aims to stabilize the electricity grid by utilizing the flexibility of end-users. To this end, end-users are offered time-varying electricity prices and incentivized for load shifting. End-users can maximize bill reduction through automated load shifting using home energy management systems (HEMSs). Since HEMS is a new technology, the future DR potential of its mass use is unknown. Here, surveys can be very useful for gaining insight into future behaviour and preferences in using HEMS. Therefore, the objective of this study is twofold: (1) to understand appliance use behaviour, electricity tariff perception, and tendency towards HEMS-based DR participation, through a survey. And then, (2) to simulate the DR potential by entering survey responses into a HEMS optimization tool. The results show that 78% of the respondents are willing to engage in HEMS-based DR. This provides the potential to reduce the peak period consumption by 33%. However, the average bill savings achieved by HEMS owners is only 6.7%, which can hinder reaching this potential. Still, 21% of the HEMS owners save more than 10% on their bills. 8% save over 15%, and 3% over 20%. These can be the target audience of the future HEMS market and DR campaigns.

Resident-Centric Distributed Community Energy Management System

This study presents a resident-centric distributed community energy management system (CEMS). More specifically, the proposed resident-centric distributed CEMS allows residents to schedule their appliances autonomously, without the need to collaborate with the community and to consider whether their appliance scheduling is optimal from the perspective of the entire community. The central controller in the proposed CEMS will then determine a solution that is optimal for the entire community by dispatching the community's distributed energy sources according to the appliance scheduling of residents. In other words, the proposed distributed resident-centric CEMS allows residents to act autonomously while securing the collective goals of the community to a certain extent. In this paper, the collective goals of the community include participating in incentive-based demand response (IBDR) events at a specific time interval, and decreasing the total electricity cost of the community in response to time-varying electricity prices. The proposed distributed resident-centric CEMS is developed using the concept of distributed optimization and mixed-integer linear programming. Different types of public loads are incorporated into the proposed framework including stoppable and deferrable public loads. The simulation results show that the proposed framework dispatches power optimally.

Public preferences and increasing acceptance of time-varying electricity pricing for demand side management in South Korea

Linking demand and supply management on the basis of public acceptance is essential to prepare for the energy transition from fossil fuels to environment friendly energy integration, and to ensure stability in electricity supply and demand management. Demand management could promote the conservation of energy resources by stabilizing electricity supplies and curbing consumer consumption, which could consequently help reduce capital expenditures. Major developed countries have improved the efficiency of electric facility operations by implementing demand management based on pricing programs. The Korean government also seeks to improve demand management by introducing time-of-use (TOU) tariffs to residential electricity consumers. However, the rate of residential customers switching to the TOU tariff in Korea is still low, which negatively affects the success of demand management. Accordingly, this study aims to consider the preference structure of residential electricity consumers for TOU pricing using a discrete choice experiment, and further derive policy implications to promote the pricing program's switching rate. The analysis of consumer preferences in this study shows that consumers prefer a TOU tariff plan that minimizes uncertainty. Therefore, it is necessary to provide consumers with information on their consumption amounts and usage patterns based on real-time metering to reduce consumer uncertainties. In conjunction with the findings from the scenario analysis on the choice probability of TOU tariffs, incentives such as subsidies for purchasing high-efficiency appliances and carbon points are an effective measure to lower the barrier to switching, and reduce additional utility expenses. Furthermore, diversifying tariff plan attributes is required in subsequent studies on public acceptance to accurately identify consumer preferences while considering the TOU tariff plan's rate of diffusion.

Demand response with active phase change material based thermal energy storage in buildings

A large potential to shift the electricity consumption to adapt to the stochastic renewable electricity generation is identified through the utilisation of a combination of Heat Pumps (HP) and local Thermal Energy Storage (TES) devices in building heating systems. In this paper, a building heating system coupled with an active Phase Change Material (PCM) TES device and a HP is simulated to characterise its potential for Demand Response (DR) applications. A control-oriented numerical model for the PCM TES is developed and previously validated numerical models of the building, HP and hot water radiators are integrated to simulate the dynamics of the coupled building heating system. A Genetic Algorithm (GA) based control strategy is designed to optimise the building heating energy consumption and operational cost with respect to time-varying electricity price signals. The developed control strategy is successfully implemented to utilise the TES capability of the PCM and the thermal inertia of the building to intelligently shift the electrical load of the HP to low price periods, while satisfying the specified indoor comfort requirements. In comparison to a reference case utilising a sensible TES, cost savings and consumption reductions of more than 40% and 30%, respectively, are attained with the active PCM TES. Simulation results indicate that utilising an active PCM TES over a sensible TES offers significant advantages for DR applications in building heating systems in terms of load shift flexibility, energy costs and consumption.

A Bi-Level Framework for Supply and Demand Side Energy Management in an Islanded Microgrid

Time-varying electricity prices in demand response (DR) programs motivate users to change their consumption behavior with the aim of electricity bill cost minimization. Coordinated DR schemes aim to flatten the aggregate load profile and mitigate rebound peaks. However, a comprehensive model which integrates supply and demand sides has to be developed to consider system constraints and renewable energy resources. In this regard, this article proposes a bi-level framework for supply and demand side energy management in an islanded microgrid. In the first level, consumers schedule their appliances’ operation to minimize their expenses. In the second level, a dynamic economic dispatch problem is solved which finds the power generation schedule for the generators resulting in the minimum electricity production cost. A game-theoretic turn-based decentralized algorithm is suggested which manages the interaction between both levels. Simulation results show that the proposed algorithm minimizes users’ electricity bill costs while satisfying power system constraints.

Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets

We study the operational problem of shared autonomous electric vehicles that cooperate in providing on-demand mobility services while maximizing fleet profit and service quality. Therefore, we model the fleet operator and vehicles as interactive agents enriched with advanced decision-making aids. Our focus is on learning smart charging policies (when and where to charge vehicles) in anticipation of uncertain future demands to accommodate long charging times, restricted charging infrastructure, and time-varying electricity prices. We propose a distributed approach and formulate the problem as a semi-Markov decision process to capture its stochastic and dynamic nature. We use cooperative multiagent reinforcement learning with reshaped reward functions. The effectiveness and scalability of the proposed model are upgraded through deep learning. A mean-field approximation deals with environment instabilities, and hierarchical learning distinguishes high-level and low-level decisions. We evaluate our model using various numerical examples based on real data from ShareNow in Berlin, Germany. We show that the policies learned using our decentralized and dynamic approach outperform central static charging strategies. Finally, we conduct a sensitivity analysis for different fleet characteristics to demonstrate the proposed model’s robustness and provide managerial insights into the impacts of strategic decisions on fleet performance and derived charging policies. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1187 .

Rolling-horizon optimization integrated with recurrent neural network-driven forecasting for residential battery energy storage operations

In recent years, the installation of battery energy storage (BES) integrated with solar photovoltaic (PV) panels in residential houses has been rapidly accelerated tied to the high penetration of renewable energy resources, and it is expected to play a significant role as distributed energy resources (DER) in smart grids and microgrid operations. To directly offset electricity demand by efficiently utilizing renewable energy, it is crucial to optimize BES operations in conjunction with forecasting uncertain renewable energy, electricity demand, and prices. Therefore, there is an urgent need to develop proper methodologies to optimize BES operations in residential settings to realize efficient and sustainable smart grid operations under the high penetration of renewable energy. Given this context, this study intends to develop a rolling-horizon optimization model integrated with a recurrent neural network-driven forecasting that is uniquely designed to interactively forecast uncertainty and optimize BES operations in an iterative fashion. Comprehensive numerical experiments have been conducted based on data collected from residential houses to evaluate the performance of the proposed optimization model. The results demonstrate that the proposed optimization model can be practically applied for optimizing residential BES operations while efficiently utilizing solar power in response to time-varying and uncertain electricity demand and prices. Moreover, because the length of the look-ahead period used for both forecasting and optimization should be appropriately set for the proposed model, experiments have been designed and conducted to investigate the impact of the length of a look-ahead period on overall performance.

Distributed ESS Capacity Decision for Home Appliances and Economic Analysis

The imbalance between the supply and demand of electricity is becoming expansive due to the increase in renewable energy penetration. Consequently, the time-varying electricity price is proposed as a tool to manage the mismatch. To cope with the electricity price difference across time and to extract the benefit to the limit, an energy storage system (ESS) can be a good tool to smooth out electricity usage. Nevertheless, ESS can be expensive due to the high installation cost and centrally managed system. In this paper, we propose a distributed ESS installed in each home appliance. Separately installed batteries can avoid sizeable initial investment costs. We calculated the benefit of the system that mainly stemmed from the electricity price difference and compared the benefits across various sizes of the battery system. In addition, the charging/discharging schedule depends on the power consumption profile and electricity price distribution. Decreasing battery prices and rising electricity price volatility can increase the system’s potential benefit.

Real-time optimal control for end-users with energy storage and renewable sources

In this article, we investigate the problem of real-time optimal control for end-users with integrated renewable energy generation and energy storage. The proposed a novel and comprehensive model consists of the electric vehicle (EV), renewable energy, the residential combined heat and power (resCHP) system and energy storage systems (e.g., battery and water tank). By jointly considering the operational constraints of energy storage, the stochastic behavior of renewable energy production and demand, and the volatility of electricity prices, the energy management problem is transformed into a stochastic optimization problem that minimizes the end-user’s the long term time-averaged cost. First, to eliminate the effect of storage control coupling over time, the capacity constraints (physical constraints) of the stochastic optimization problem are relaxed to time-averaged constraints. Secondly, we solve this problem by designing an real-time online algorithm based on the improved Lyapunov optimization. The proposed algorithm has a significant feature that does not require any statistical information about the stochastic system inputs (e.g., the uncertain power and heat demand, the renewable energy production, and the time-varying electricity prices, and so on). Furthermore, the performance analysis of the proposed algorithm shows that it not only ensures that all control decisions are executed within the limited storage capacity, but also provides a near-optimal solution for the optimization problem. Finally, simulation results demonstrate that the proposed algorithm can reduce 74.4% of the total cost compared with the benchmark approach.

A joint optimisation model for charger locating and electric bus charging scheduling considering opportunity fast charging and uncertainties

The continuous improvement of charging technologies makes fast charging applicable to overcoming the limited driving range of electric vehicles by providing en-route opportunity charging. Fast chargers can be deployed at bus stops to top up the battery for an electric bus during its dwelling time when passengers board and alight, which allows the usage of small size battery and avoids deadheading trips to depot or charging station for recharging. This paper examines the joint optimisation problem of locating fast chargers at selected bus stops and developing charging schedule to support electric bus operation considering time-varying electricity price and penalty cost if passengers’ extra waiting time, the delay time of starting a trip or the excessive travel time of completing a trip is caused by charging activities. To handle the uncertainties associated with the travel time and passenger demand, a robust model is proposed and formulated. The proposed methods are applied to multiple bus routes in Sydney in the numerical studies. The numerical results show that frequent charging activities occur at both intermediate and last stops during bus service trips, indicating that it is beneficial for buses to utilise passengers’ boarding and alighting time at intermediate stops and resting time at the last stop between two adjacent trips to top up battery. Sensitivity analysis on a series of system parameters, such as the acquisition cost of chargers, amortized battery price and charging power, is conducted. The numerical results verify the feasibility of deployment of fast chargers and corresponding top-up charging activities at both intermediate and last stops. Electric buses outperform the conventional diesel buses regarding energy consumption cost, especially considering the increasing diesel fuel price and its costly environmental impacts. Moreover, charging activities at intermediate stops will become more common when passenger demand (the volume of boarding and alighting passengers) increases at intermediate stops.

Making the Best of the Second-Best: Welfare Consequences of Time-Varying Electricity Prices

In electricity markets, the price paid by retail customers during periods of peak demand is far below the cost of supply. This leads to overconsumption during peak periods, requiring the construction of excess generation capacity compared to first-best prices that adjust at short time intervals to reflect changing marginal cost. This paper investigates the response of infrequently studied small commercial and industrial establishments to a four-hour increase in retail electricity prices invoked by individual utilities during peak demand periods 15 times per year. This policy intends to reduce electricity consumption when generation costs are highest. I find that the approximately tripled prices reduce establishment peak electricity usage by 13.5%. Using a model of capacity investment decisions, I find that the program delivers 44% of the benefits of the first-best policy of continuously varying prices and suggest two simple improvements in program design that could nearly double these welfare gains.

Real‐time pricing response attack in smart grid

Real-time pricing is an attractive tariff for demand response programs, in which consumers could provide load flexibility by adjusting their electricity consumption in response to time-varying electricity prices. This paper reveals a new threat called real-time pricing response attack (RPRA), which manipulates consumers' electricity consumption by tampering with electricity price signals. Simulations are performed in IEEE 24-bus, 39-bus, and 118-bus systems, and the results show that 8% to 28% of the transmission lines could be overloaded by the attack, which could cause serious security problems. Results of sensitivity analysis indicate that the attack effects are influenced by various factors, such as DR models, price elasticity, line capacities etc. To defend against the RPRA attack, we have proposed various detection and mitigation strategies.

Dynamic energy scheduling for end-users with storage devices in smart grid

In this paper, we endeavor to address the problem of dynamic energy scheduling scheme for end-users with storage devices in smart grid. An end-user with an energy storage device is developed, which draws energy from multiple energy sources: local energy suppliers and external power grid. Our goal is to minimize the end-user’s total costs (including energy usage cost and storage cost) over long-term time under uncertainty (time-varying electricity price, uncertain energy output and demand). Concerning the energy storage cost, the resulting problem is modeled as a stochastic optimization case. Following this trend, based on the improved Lyapunov optimization, a dynamic energy scheduling algorithm with lower complexity is developed. It is theoretically proved that the proposed algorithm can make the optimization target asymptotically close to optimum. Simulation results are given to demonstrate that proposed algorithm can significantly save total costs than existing schemes where energy scheduling without the energy storage cost constraint.

Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment

Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme.