Demand Response Policies Research Articles

Integrated management of electric vehicle sharing system operations and Internet of Vehicles energy scheduling

The sharing of electric vehicles and the Internet of Vehicles both positively impact societal benefits. However, the complexity, uncertainty and multi-directionality of transport–energy coupled systems greatly increase the difficulty of integrated management. This paper proposes a multi-objective robust optimization framework to solve the integrated management issue of electric vehicle sharing system operations and Internet of Vehicles energy scheduling under uncertain information, in which components such as renewable energy, microgrids, autonomous driving and their impacts are fully considered. To enhance the capability of integrated management and the synergy between supply and demand, a charging and discharging scheduling strategy that considers a converged demand response policy is proposed. Numerical analyses using real data provide valuable validation and insights. The proposed method and scheduling strategy enable the implementation of integrated management in photovoltaic microgrids and significantly improve economic and environmental benefits. Meanwhile, the microgrid system can operate in an ”off-grid mode”, which enables a multi-energy complementary mechanism. Additionally, electric vehicles release a great deal of scheduling flexibility through demand-side management, which combined with electric energy storage, can efficiently local consumption of photovoltaic power.

Impact of maximized utility benefit based on customer willingness for economic operation of a grid connected microgrid system

Optimal scheduling of distributed energy resources (DERs) to obtain a minimized generation cost of a low voltage (LV) microgrid (MG) system has always gravitated the power system optimization researchers. This paper proposes a dual level optimization framework which emphasizes on maximizing the DISCOM profit in the first level and later minimizes the generation cost of the MG system. The MG system considered for the study consists of renewable energy sources, fossil fueled generators, micro turbine and fuel cell and the cost components involve depreciation cost, fuel cost, penalized emission cost and operation and maintenance (O&M) cost. An incentive based demand response (IBDR) policy is implemented which utilizes customer’s willingness to curtail their load demands and offers incentives for the same. When the DISCOMs share of profit is maximized using IBDR policy in level one, the modified load demand of the MG system obtained by deducting the curtailed load from the forecasted load is considered to minimize the generation cost of the MG system in the second level. A recently developed swift and facile Arithmetic optimization algorithm (AOA) is used as optimization tool for the study. The maximum DISCOM benefit was found to be 211▪ when the customers curtailed 105 kWh energy as per their willingness. Literatures reported the minimum power generation cost of the MG system as 880▪, 871▪ and 807▪ for base load, load shifting based demand side management (DSM) and customer incentive-based DR policies respectively. The proposed IBDR policy outperformed the reported literatures with a minimal generation cost of 768▪. Measures of central tendencies also corroborate to the robustness and efficiency of AOA.

Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni

The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users’ participation at different levels and limiting the potential with various constraints.

Household responses to winter heating costs: Implications for energy pricing policies and demand-side alternatives

I conduct a survey that presents research subjects with hypothetical costs to adjust their thermostats. I estimate responses to the cost of heating and analyze the causes for heterogeneity in household demand for energy services using the survey results as a complete-information baseline. I find that even at the highest price level ($8 per 5 °F or 2.8 °C), half of the participants exhibit zero response to price. On average, a 100 percent increase in the marginal cost of heating the home induces a 0.31 to 0.97 degree Fahrenheit (0.17 to 0.51 °C) reduction in the winter heating level, corresponding to a −0.005 to −0.014 elasticity. Further, I find that participants’ survey behavior with complete information can explain observed real-world temperature settings, suggesting a limited role for informational barriers or salience issues in energy-service demand heterogeneity. Inelastic demand suggests that energy efficiency policies may have high returns and that centralized demand–response policies may be required to address winter energy emergencies.

How consumers value improving energy efficiency policy in the electricity market: A contingent valuation experiment in South Korea

Global warming concerns have emphasized the need for carbon neutrality. Improving the efficiency of the electricity industry is one method of reducing CO2 emissions. There is a demand response policy that trades the saved electricity through voluntary adjustment of consumption patterns. The introduction of real-time pricing (RTP) based on advanced power supply systems is a method of transitioning to a demand-oriented power system. However, as Korea's power supply system is a relatively less efficient single-rate system, it is necessary to improve the efficiency of demand management. To transition to a demand-oriented power system in Korea, consumer preference is first investigated. This study evaluates the willingness to pay (WTP) for the introduction of RTP in Korea. Based on a survey of 500 households using the contingent valuation method, the monthly average WTP was found to be 2465 Korean won per household ($2.18 per household), which is approximately 7.8% of the electricity rate. Several additional factors influencing WTP were analyzed. Further analysis indicated that it is more effective to establish infrastructure for introducing RTP in local areas with low average electricity rates. These findings are beneficial for the development of future demand response policies.

Optimization of Microgrid Energy Cost with Electrical Vehicles and Demand Response

Challenges with current power systems include increasing energy costs and Greenhouse gasses. Participation in distributed generators (DGs) and demand response (DR) strategies inside the distribution network may solve these issues. This paper's primary objective is to suggest an energy management system (EMS) strategy for campus microgrid reduce all the issues mentioned above. To practice this strategy a microgrid is considered. Conventionally the microgrid uses a utility grid to fulfill load demands. But in the proposed model the integration of a micro turbine, photovoltaic (PV) system, battery energy storage system (BESS), and Electric vehicles is proposed. Mix integer linear programming MILP technique is used in MATLAB to solve the non-linear optimization problem. The energy management system EMS and demand response policies are used to optimally schedule the available resources and reduce energy costs. The integration of EVs is also observed as source and load. The simulation results are discussed and different case studies are analyzed. Simulation results show that 46% cost saving is observed while using EVs in the system as source. The real-time energy interruption problem is also observed in this paper.

Energy management for solar-hydrogen microgrids with vehicle-to-grid and power-to-gas transactions

This study addresses the energy management of an integrated solar-hydrogen microgrid. The microgrid includes zero-emission vehicles, renewable energy sources, an electrolyzer, bidirectional charging stations, and a hydrogen refueling station with hydrogen storage. Vehicle-to-grid (V2G) charging stations can alleviate renewable electricity variability by discharging the energy of vehicle batteries back to the grid. The electrolyzer can absorb excess solar generation to balance supply and demand with power-to-gas (P2G) transactions. Given the uncertainties from intermittent renewable generation and energy demand, a two-stage stochastic optimization framework derives the optimal power management strategy for the component subsystems, aiming to minimize operating costs. The first stage of the model determines the day-ahead charging price and power supply for electric vehicle charging and hydrogen production, and the second stage performs real-time energy scheduling under different scenarios. The performance of the proposed optimization strategy is examined through a case study using real-world data, and the results demonstrate that daily operating costs can be reduced by up to 27.5%. Moreover, inclusion of the P2G process with hydrogen storage yields better performance to support peak shaving and reducing costs compared to using V2G or dynamic pricing-based demand response policies only.

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis.

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage.

Negawatt Planning for Aggregation Coordinator Considering Fluctuations in Electricity Demand of Consumers

Power suppliers generate electricity to ensure that the power generated equals demand. The vast daily fluctuations in electricity demand result in very high generation costs. Demand response is attracting attention as a solution to these problems in recent years with the spread of renewable energy, energy resources such as storage batteries, and the development of IoT technologies. Demand response means changing the power demand pattern by controlling demand side energy resources and distributed energy resources. In recent years, the use of demand response and the construction of mechanisms for it are being promoted. In this study, we focus on negawatt trading that is one of the demand response policies. We formulate a stochastic programming model for negawatt planning operation and the efficient solution method are shown. From experiment results, it is shown that the worst situation can be avoided using CVaR model. We also showed the efficiency of solution.

A comparative analysis of patterns of electricity use and flexibility potential of domestic and non-domestic building archetypes through data mining techniques

The large-scale deployment of smart meters has led to significant amount of electricity demand data available, driving it into the realm of Big Data. It is a major challenge to exploit this Big Data in order to characterise electricity use patterns and to support demand response policies. In this paper, we perform a featured-based cluster analysis on nine building archetypes (hospitals, schools, offices, hotels, flats, houses etc.) to identify electricity use patterns. Then, four metrics are developed, which are entropy, load curviness, peak intensity and index of hourly ramp rates, to measure these archetypes’ suitability to be involved in demand response schemes. A significant difference in electricity use patterns between the archetypes is found, as well as among the seasons and days of the week. We present a number of metrics for each archetype to establish which type of archetype should be prioritised for demand response programmes in terms of peak management, ramp rates as well as demand flexibility. A key finding of our study is that households offer more demand flexibility than the non-domestic sector and should therefore be incentivized to participate in dynamic electricity tariffs.

Multi‐objective electrical demand response policy generation considering customer response behaviour learning: An enhanced inverse reinforcement learning approach

Demand response (DR) is an effective load management method. To attract customers to participate, DR policies need to both satisfy customers' individual DR habits and be economically profitable. However, customers’ individual DR habits are hard to be formulated with few hypotheses when other objectives are simultaneously considered. To tackle this challenge, a novel DR behavioural learning method is proposed. We learn customers’ DR habits by an inverse reinforcement learning (IRL) method to reduce the subjectivity in DR model formulation. Meanwhile, in contrast to traditional learning-based methods, the proposed method can adapt to multiple DR objectives more than just following customers’ DR habits, like obtaining higher economic revenues. Additionally, we consider the diversity and changes of customer DR behaviour patterns and offer an enhancement for the proposed DR behavioural learning method via building a DR pattern clustering and inference module. The proposed method can work with customer-side energy storage systems to diversify the DR policies and make the DR behaviours more flexible. Case studies show the proposed method can reduce about 10–20% behavioural learning deviations than the compared model-based methods, while daily charges of the proposed method can be further reduced by over 4% than the compared supervised-learning-based methods.

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits

Massive adoption of shared electric mobility benefits people’s daily commute and environment but creates overload issues into the power grid, then further cause challenges to charging service operations and power management. Previous research always focuses on single optimization process on shared vehicle planning, rather than the combination of demand management into day-ahead planning operations. To this end, we attempt to propose a mixed integer programming model integrating demand response operations to further explore the impacts of demand response on shared electric vehicle planning operations. We first model a two-stages model integrating charging facility location in the first stage and vehicle relocation in the second stage. Moreover, both supply-side and demand-side uncertainties are considered and approximated into tractable form by applying sample average approximation and distributional robust set featuring the entropy knowledge and electric vehicle’s multi-level charging duration. The demand response policy is also proposed to reshape the original charging demand into an economical and reliable way to improve operational efficiency and mitigate the power overload issues caused by massive electric vehicle adoption. Further, we conduct a real-world case study in Amsterdam, the Netherlands, to explore the social-operational impacts of vehicle planning optimization model integrating the demand response, robust charging facility planning in three areas: (1) The demand response integration promote electric vehicle planning operations on cost-saving for about 3%. (2) Data richness of serviceability towards charging piles influence all decisions through the shared electric vehicle charging station planning. (3) A trade-off exists between technical progress on charging rate and charging technology stability.

Joint Computation Offloading and Demand Response Management in Mobile Edge Network With Renewable Energy Sources

Mobile Edge Computing (MEC) networks have recently developed to integrate renewable sources for providing sustainable and low-cost computation/energy services. However, one challenging problem is how to guarantee the stable and effective services for numerous devices under highly intermittent renewable generations and variable computation/energy demands. In this paper, we consider to co-deploy the edge servers and energy storage at micro Base Station (BS) in each edge sub-network. In the considered scenario, we first determine the number of edge servers and energy storage units at different sub-edge networks in a long-term period. Then, we jointly design the offloading and demand response policies for all micro BSs in a centralized manner. Considering the variabilities of renewable energy and computation/energy demands, the system cost is minimized while meeting the energy demand-supply balance and renewable portfolio standards. Furthermore, a distributed optimization problem is formulated to find the optimal offloading parameters, the amount of charging/discharging energy and utilized renewable energy of the micro BS without handing over all operational information. Numerical results show that the proposed deployment and policies can provide effective offloading and energy saving strategies for the proposed mobile edge network.

Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements

The electric power sector significantly contributes to China's economic growth. However, rapid industrialization and urbanization have led to energy shortage problems. More than 70% of customers' demand is fulfilled by fossil fuel sources that threaten the environment. The rapid increase in demand and the need for decarbonization has forced China to work on demand-side management (DSM) to fulfill the power needs of the country. DSM is receiving much importance in China over the past few years. However, it is still in the early phases of development in comparison to other developed countries. At first, this paper points out the need for demand response, which is an aspect of the DSM, for maintaining power system stability and integrating variable renewable energy resources such as wind (on-shore and off-shore) and solar (photovoltaic and concentrated solar power). Then, it assesses China's power sector reforms and changes in demand response policies over the period to promote demand response programs. It also reviews the current demand response pilot projects in China, identifies technical and policy barriers in implementing these projects and proposes recommendations to make it successful across the whole country.

How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires

Incentive-based demand response (DR) policy plays an important role in guiding residents' electricity consumption behavior. How to effectively implement the DR policy has become a scientific issue that needs to be addressed urgently. To this end, based on the data from large-scale DR trials and matching questionnaires, the policy implementation path has been analyzed. The results show that households responded to the DR policy saved 0.09 kW h more electricity in the 1.5-h response period than households that did not respond to the DR policy. On the management implementation side, the subsidy price is crucial. Community publicity can also enhance the electricity saving effect. It also has been found that the electricity-saving potential of the low-level community is relatively limited. On the households' response side, households with higher household incomes, younger income earners, more air conditioners and small appliances, and higher gas consumption have higher policy participation. The novelty and originality of this article is that the data collected through large-scale controlled trials are unique and valuable. And we creatively combined trial data and surveys data together, which will enable us to further explore the implementation-side management factors and response-side household attributes. At the end, we put forward systematic policy recommendations for the implementation of DR policy, which has important references significance for countries with similar regulated electricity markets. • Three trials have been conducted with up to 20,000 participating families. • Experimental research and survey data have been effectively integrated. • Influencing factors of residential demand response policy have been analyzed. • Implementation-side factors and response-side attributes have been analyzed. • Policy references for countries with similar regulated electricity markets.

Risk-Averse Home Energy Management System

Transformation of conventional energy systems into smart grids enables the integration of residential buildings with distributed generations, electro-thermal storages and demand response policies. Further, it improves the household comfort level and helps to preserve the ecological system. Keeping in view the techno-financial impact of residential energy management, optimal handling of residential customers may prove meaningful for peak load reduction, valley filling, and energy conservation. In this regard, this paper devises a residential energy management system (EMS) to optimally schedule appliances, energy sources and electro-thermal storage for reduction of consumption cost and greenhouse (GHG) emissions. Building integrates national grid, natural gas network and solar energy as input carriers, whereas, electricity, heat and cooling as output carriers. To resolve the risk of loss of load, conditional value at risk (CVaR) has been incorporated in the objective function. Comparison demonstrates that under risk-averse approach, energy retaining capability of electric vehicle and thermal energy storage increases by 28.56% and 53.34%, respectively. This stored energy acts as a reserve during absence of solar irradiance and outages on electric and natural gas networks. Moreover, to make EMS more efficient in tracking optimum solutions with faster convergence speed, a hybrid algorithm has been devised by concatenating the modified flower pollination algorithm with mixed-integer linear programming. The proposed algorithm has been validated by comparing its results with the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization and Two Cored Flower Pollination Algorithm (FPA). Results manifest that the cost, GHG emissions and execution time drop by 8.98%, 10.81% and 35.064%, respectively.

Infrastructure Planning of Photovoltaic Charging Stations

Problem definition: Nonrenewable energy is typically recognized as ''dirty energy'' with respect to the environment such that most countries reduce their nonrenewable facilities and then improve their renewable energy adoption. In this paper, we study and develop effective hybrid energy generation and charging operations strategies with the aim of exploring and enhancing the environmental benefits of nonrenewable energy integrated electric vehicle (EV) charging service in a planning problem for photovoltaic (PV) charging stations. Academic/Practical relevance: Our research contributes to the literature on and practice of sustainable operations management by developing an integrated stochastic robust planning model that allows us to explore its implications in three areas: (1) the environmental benefits of nonrenewable energy synthesis, (2) integrated modeling with processor-sharing queuing to optimize the EV charging service coupled with renewable energy power scheduling, (3) the performance of proposed demand response (DR) policies that incorporate the features of intermittent energy, such as unstable solar resources. Methodology: We characterize the fleet charging dynamics by adopting a processor-sharing queuing network in a mixed integer programming (MIP) integrated planning model. We model the uncertain solar resources in a worst-case expectation weighted by geometric-like probabilities obtained by processor-sharing queuing modeling. We then develop its linear equivalent form to approximate a tractable optimization model. We also adopt a multinomial logit (MNL) model to estimate customers’ charing utilities and the incentive payment across time slots in our integrated DR policy. Results: In a real-world case study of CAR2GO in Amsterdam, we find that nonrenewable energy integration improves clean energy utilization and provides a reliable and cost-effective EV charging service under current PV charging practice. The results also demonstrate the advantages of the proposed DR policy in incorporating uncertain solar resources on the reliability, profitability and environmental benefits of the EV charging service. Managerial implications: In their efforts to improve renewable energy planning, policy-makers should be more prudent about giving up nonrenewable energy. Certain reasonable features - synthesizing DR operations coupled with efficient energy scheduling - can alleviate the adverse impacts of intermittency and uncertainties that plague renewable energy and, in turn, gradually improve the quality of EV charging services.

Optimal Scheduling for Campus Prosumer Microgrid Considering Price Based Demand Response

Existing energy systems face problems such as depleting fossil fuels, rising energy prices and greenhouse gas (GHG) emissions which seriously affect the comfort and affordability of energy for large-sized commercial customers. These problems may be mitigated by the optimal scheduling of distributed generators (DGs) and demand response (DR) policies in the distribution system. The focus of this paper is to propose an energy management system (EMS) strategy for an institutional microgrid (μG) to reduce its operational cost and increase its self-consumption from green DGs. For this purpose, a real-time university campus has been considered that is currently feeding its load from the national grid only. However, under the proposed scenario, it contains building owned solar photovoltaic (PV) panels as non-dispatchable DG and diesel generator as dispatchable DG along with the energy storage system (ESS) to cope up with the intermittency of solar irradiance and high operational cost of grid energy. The resulting linear mathematical problem has been mapped in mixed-integer linear programming (MILP) and simulated in MATLAB. Simulations show that the proposed EMS model reduces the cost of grid electricity by 35% and 29% for summer and winter seasons respectively, while per day reductions in GHG emissions are 750.46 kg and 730.68 kg for the respective seasons. The effect of a half-sized PV installation on energy consumption cost and carbon emissions is also observed. Significant economic and environmental benefits as compared to the existing case are enticing to the campus owners to invest in DG and large-scale energy storage installation.