Demand Response Strategy Research Articles

Towards energy transition: A novel day-ahead operation scheduling strategy for demand response and hybrid energy storage systems in smart grid

Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the incorporation of carbon capture technologies into the traditional thermal power plants holds great potential in emissions reduction. In this paper, the integration of renewable energy sources (RES) and coal-fired power generation units outfitted with carbon capture schemes is addressed. Multiple demand response (DR) programs and hydropower plants are strategically utilized to increase the power system flexibility. To effectively plan the day-ahead (DA) operation of the power system, a presumed market-clearing framework is adopted and modelled as a risk-constrained two-objective stochastic mixed-integer linear programming problem. The proposed framework helps to tackle the uncertainties related to RES and demand variations by employing a hidden Markovian process (HMP) technique. To simultaneously minimize the system’s operational costs and CO2 emissions, an enhanced version of the augmented ɛ-constraint method is employed. To prove its value, the proposed framework is devoted to the 24-bus IEEE reliability test system (IEEE-RTS). The system features substantial penetration of RES (exceeding 87% of peak load) and standard DR options capacities (less than 25% of peak load). The results show a 24% reduction in load peaks, an over 63% decrease in emissions, and a 17% reduction in the overall operation costs.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques

The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable.

Demand Response Strategy Based on the Multi-Agent System and Multiple-Load Participation

In order to improve the utilization of user-side power resources in the distribution network and promote energy conservation, this paper designs a distributed system suitable for power demand response (DR), considering multi-agent system (MAS) technology and consistency algorithms. Due to the frequent changes in the power system structure caused by changes in the load of a large number of users, this paper proposes using cluster partitioning indicators as communication weights between agents, enabling agents to utilize the distribution network for collaborative optimization. In order to achieve the integration of multiple load-side power resources and improve the refinement level of demand-side management (DSM), two types of agents with load aggregator (LA) functions are provided, which adopt the demand response strategies of Time-of-Use (TOU) or Direct Load Control (DLC) and model the uncertainty of individual device states using Monte Carlo method, so that the two typical flexible loads can achieve the target load-reduction requirements under the MAS framework. The research results demonstrate that this method achieves complementary advantages of the two types of loads participating in DR on a time scale, reducing the costs of power companies and saving customers’ electricity bills while peak shaving.

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

Blockchain for the Energy Transition

The massive impact of the wind and PhotoVoltaic (PV) power on the power systems require new approach and strategies, because of their unpredictability. This large expansion has been due to the feed-in tariffs, financial support and a concentration of R&D activities. Moreover, the distributed energy sources require a pervasive and complex management of the energy exchanges. Until now, the management has been usually based on a central authority, but new paradigms based on the BlockChain (BC) technology are very promising. This approach can also be useful to manage the peak power as well as the Demand-Response (DR) strategies. The paper presents this challenging tool for the current energy transition, highlighting the intrinsic features of the blockchain technology and the opportunities for the future energy systems, e.g. the execution of smart contracts in Peer-to-Peer (P2P) networks. This approach will become increasingly useful to manage the energy exchanges in new contexts, where the number of prosumers and active users is growing. The paper also proposes an application of the BC to manage the peak demand, using the energy injected into the grid by the electric vehicles.

Balancing urban energy considering economic growth and environmental sustainability through integration of renewable energy

This paper delves into the critical intersection of urban energy management, economic growth, and environmental sustainability through the integration of renewable energy sources in smart urban homes. The escalating demand for power has created an energy scarcity, necessitating innovative solutions to balance supply and consumption. In this context, the transition from traditional electric grids to smart grids (SG) is gaining prominence. To harness the potential of smart urban homes, particularly in the context of renewable energy, solar panels on rooftops have become indispensable. Smart grid technologies enable users to engage in demand response strategies, offering incentives and financial benefits for flexible device usage. This study introduces a novel optimization algorithm, the Polar Bear Optimization Algorithm (PBOA), designed to schedule domestic device usage patterns efficiently. To evaluate the effectiveness of PBOA, this paper conducts comparative analyses with other optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization Algorithm (PSOA). Energy cost metrics, including Critical Peak Price (CPP) and Real-Time Price (RTP), are employed to assess cost reduction strategies. The primary focus of this study is to minimize energy costs and reduce the Peak to Average Ratio (PAR), thereby promoting economic growth and environmental sustainability in smart urban homes. It should be noted that neural network (NN) machine learning technique has been used for forecasting the weather and renewable energies output power.

A novel demand response-based distributed multi-energy system optimal operation framework for data centers

To address the increasing energy demands in data centers, distributed multi-energy system (DMES) can provide an efficient solution. This study proposes a DMES coupled by photovoltaics, wind turbines, ground source heat pump, internal combustion engines, electric chillers, absorption chillers, and energy storage device. The optimal scheduling of DMES with source-load interaction plays a crucial role in improving the performance of data centers. To achieve this, an integrated demand response (IDR) strategy is established, considering time-of-use pricing and renewable energy peak subsidies. Then, a clean and economical optimal operation model for DMES based on proposed IDR strategy and carbon trading mechanisms is established. Furthermore, to enhance the robustness of scheduling, this study analyzes the impacts of uncertainties in renewable energy output and demand response on DMES operation using the Monte Carlo method and interval method. Case study results demonstrate that the proposed IDR strategy increases electricity and cooling loads while reducing heat loads during the peak period of renewable energy generation, compared to the traditional IDR strategy. The proposed scheduling model, which incorporates the proposed IDR strategy and carbon trading mechanism, improves data center performance by reducing total costs by 1.85%, carbon emissions by 0.56%, electricity purchases by 2.93%, while increasing renewable energy utilization by 0.77%. The results also highlight the critical role of energy storage devices, with an optimal initial charge state is 0.5. Additionally, optimization considering multiple uncertainties, results in higher total costs and broader interval widths compared to single-factor uncertainty optimization, but reduces system operational risk.

An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty

This paper proposes a novel mathematical optimization model aimed at incorporating demand response strategies in the operational scheduling problem of Electric Vehicle (EV) Battery Swapping Stations (BSS). The primary goal of the model is to minimize the operational costs while accounting for intricacies arising from diverse charging modes, variations in battery capacities, charger availability, different states of charges (SOC) of incoming discharged batteries, time-of-use power prices, power constraints and fluctuating customer demand patterns. Furthermore, the model takes into consideration the effect of faster charging modes on battery health to ensure a sustainable operation. The proposed model is further extended to incorporate the effect of swapping demand uncertainty on the operational decision-making of the BSS, and a two-stage stochastic optimization model with a recourse approach is proposed. The proposed stochastic optimization model is tested with relevant case studies, and the results demonstrate the efficacy of the model in providing a risk-neutral solution by determining the optimal demand satisfaction pattern and calculating the number of additional batteries needed to minimize demand shortfalls across all scenarios. The proposed scheduling framework holds the promise of improving both the efficiency of the BSS operations and customer satisfaction, paving the way for accelerated EV adoption.

Impacts of multiple demand-side management strategies on microgrids planning: a literature survey

Abstract Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.

The Sub-Metered HVAC Implemented for Demand Response Dataset

Abstract Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research.

Energy, economic and environmental benefits of Demand Response for improving building energy flexibility

The increasing penetration of intermittent renewable sources in power generation at local and building-level poses growing issues in balancing generation and demand. To avoid imbalances, it is therefore necessary to ensure adequate levels of flexibility in the building energy system. This can be done both on the generation side, through the coupling of different energy carriers (cogeneration, power-to-heat solutions) and/or the integration of storage systems, and on the demand side, through smart “demand response” programs. This paper considers a tourist facility located in central Germany as a case study to evaluate the energy, economic and environmental benefits that can be obtained from the application of appropriate demand response strategies. The electrical demand data of the facility are monitored at both aggregate and individual load levels and made available by means of a cloud platform. The facility includes two stationary combined heat and power internal combustion engines powered by natural gas and a photovoltaic system. The results show how, thanks to appropriate load management, it is possible, on the one hand, to increase the self-consumption of PV-generated energy and, on the other hand, to keep more constant the load of the engines, which can therefore operate with better efficiencies. This results in both a reduction in energy expenses and a decrease in carbon dioxide emissions attributable to the building.

Thermoelectric Optimization of Integrated Energy System Based on Bidirectional Electricity-to-Hydrogen Technology

Abstract Addressing the challenges of high energy consumption and low efficiency in integrated energy systems, this research develops an optimal scheduling strategy that takes into account the dynamic operational conditions of the system and the complementary nature of electricity, hydrogen, and heat energies. Through the innovative application of variable efficiency gas turbines and solid oxide batteries, our model adeptly converts between these forms of energy. Moreover, we introduce a demand response strategy that seeks to balance efficiency with low-carbon generation and integrates a tiered carbon trading scheme to influence user electricity use and curb system-wide carbon footprints. Empirical results validate the effectiveness of our method in significantly enhancing energy use efficiency, cost savings, and emissions reductions.

Expanding Building Archetypes to Estimate the Indoor Environment Thermal Storage Capacity in the Danish Building Stock when Performing Demand Response

Short-term demand response leveraging the energy flexibility of the building stock is a key solution to decarbonise and ensure the reliability and sustainability of smart energy grids dominated by intermittent renewable energy sources. Most of the building demand response capacity depends on different forms of energy storage in the built environment. In addition to electric batteries, hot water storage tanks, ground source heat exchanger or dispatchable on-site renewable energy sources, buildings possess a tremendous thermal storage capacity within the thermal inertia of their structure and indoor environment. The latter can easily be harnessed with heating/cooling indoor temperature setpoint modulation strategies. However, there is a clear lack of large-scale estimates of this indoor environment thermal storage capacity in the different building stocks. To tackle this issue, detailed dynamic numerical models of the different building typologies in European countries are generated to assess the potential of demand response strategies at nationwide levels. The current paper presents the preliminary results on the thermal storage capacity in the Danish building stock when performing heating temperature setpoint modulation. These results are based on estimates of the effective thermal inertia of the different building archetypes in Denmark. They show that the Danish building stock contains immense thermal storage capacity, which is comparable to all combined batteries in a large fleet of electric vehicles or all industrial-size storage tanks in district heating plants.

Demand Response and HVAC Controls in Smart Grid Integration

This extensive research paper meticulously investigates the intricate synergies between demand response strategies and Heating, Ventilation, and Air Conditioning (HVAC) controls within the dynamic realm of smart grid integration. As the global energy landscape undergoes transformative changes driven by technological advancements and sustainability imperatives, the study critically examines the symbiotic relationship between consumers, HVAC systems, and the smart grid. The paramount importance of this interaction is underscored, emphasizing its pivotal role in achieving elevated levels of energy efficiency, grid reliability, and sustainability. The research explores the multifaceted potential inherent in demand response programs and advanced HVAC controls, unraveling their collective capacity to reshape the energy ecosystem. It goes beyond the theoretical framework by delving into real-world applications, case studies, and cutting-edge technological advancements. By doing so, the study seeks to provide not only a theoretical understanding but also practical insights into the dynamic and evolving landscape of demand-side management. This investigation is poised to contribute significantly to the existing body of knowledge by illustrating how the harmonious coordination of consumer behaviors and HVAC systems can be strategically harnessed. This strategic collaboration aims to optimize energy consumption, mitigate peak loads, and contribute substantively to the establishment of a more sustainable, adaptive, and responsive energy infrastructure. As the global community grapples with the imperative to transition towards cleaner and more efficient energy systems, this research positions itself as a valuable resource for policymakers, researchers, and industry stakeholders seeking innovative solutions for a resilient and sustainable energy future.

Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand

As a new delivery mode, the collaborative delivery of packages using trucks and drones has been proven to reduce delivery costs and delivery time. To cope with the huge cost challenges brought by strict time constraints and ever-changing customer orders in the actual delivery process, we established a two-stage optimization model based on different demand response strategies with the goal of minimizing delivery costs. To solve this problem, we designed a simulated annealing chimp optimization algorithm with a sine–cosine operator. The performance of this algorithm is improved by designing a variable-dimensional matrix encode to generate an initial solution, incorporating a sine–cosine operator and a simulated annealing mechanism to avoid falling into a local optimum. Numerical experiments verify the effectiveness of the proposed algorithm and strategy. Finally, we analyze the impact of dynamic degree on delivery cost. The proposed model and algorithm extend the theory of the vehicle routing problem with drones and also provide a feasible solution for route planning, taking into account dynamic demands and time windows.

A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy

Under the policy of renewable portfolio standard (RPS), many microgrid (MG) users with high electricity consumption are installed with renewable energy (RE) generation devices to complete their RE consumption responsibility and reduce carbon emissions. In view of the conflict between the goals of microgrid operators (MGO) and MG users, it is a challenge to achieve economic optimization for both MGO and MG users at the same time when they participate in system dispatch and complete RE consumption responsibility. Therefore, this paper proposes a bi-level noncooperative dispatch strategy that takes into account user satisfaction and RE consumption willingness. In this bi-level model, the upper level is led by an MGO who decides electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. First, a novel comprehensive evaluation model is formulated to evaluate users' willingness to consume green electricity. Based on this, users' consumption behavior of green and thermal electricity is modeled independently not only in electricity trading but also in power dispatching. Second, optimization objectives are established with the goal of maximizing MG users' welfare, including user satisfaction improvement and energy purchase cost reduction, as well as maximizing the revenue of the MGO. Then, a Stackelberg game model is constructed, and a differential evolution algorithm nested CPLEX is used to solve the Stackelberg game problem of balancing the interests of both parties. Finally, our simulation analysis shows that under the dispatch strategy in this paper, load peak-valley difference is reduced. In addition, when the green electricity generated within the system is sufficient, based on a high level of users' willingness to consume green electricity, the total consumption of green electricity is increased by 29.5% and the overall welfare of users is increased by 0.75%.

High-resolution electric power load data of an industrial park with multiple types of buildings in China

Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.

Peer-to-peer energy sharing model considering multi-objective optimal allocation of shared energy storage in a multi-microgrid system

A novel peer-to-peer (P2P) energy sharing model incorporating shared energy storage (SES) is proposed in order to effectively utilize renewable energy sources and facilitate flexible energy trading among microgrids. The model is divided into three main blocks. In the first block, a multi-objective optimal allocation scheme for SES is developed to maximize SES benefits, minimize carbon emissions, and enhance the sustainability of the energy sharing zone (ESZ). The second block introduces a new internal pricing mechanism based on the energy supply and demand ratio. Further, an innovative optimal scheduling strategy for the ESZ is presented in the third block, which includes demand response strategies for residential microgrids and control strategies for SES, with the goal of minimizing the energy cost of a multi-microgrid system (MMS). To solve the proposed complex and constrained model, a multi-objective slime mould algorithm based on generalized opposition-based learning and reference points is proposed. Simulation results demonstrate that the introduction of SES and energy sharing can reduce MMS's energy cost by 89.45 % and increase the sustainability index of ESZ by 9.08 %. The proposed P2P trading mechanism can strictly lock the internal trading prices within the grid prices, which fully mobilizes the microgrids to participate in P2P trading.

EV Hosting Capacity Enhancement in a Community Microgrid Through Dynamic Price Optimization-Based Demand Response.

Community microgrids, as an emerging technology, offer resiliency in operation for smart grids. Microgrids are seeing an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can easily overwhelm such electric networks. In this work, we propose an efficient demand response (DR) scheme based on dynamic pricing to enhance the capacity of the microgrid to securely host a large number of EVs. A hierarchical two-level optimization framework is introduced to realize the DR scheme. At the upper level, the dynamic prices for the participating users in DR are optimized while at the lower level, each user optimizes its energy consumption based on the price signal from the upper level. An evolutionary algorithm and a mixed-integer linear programming model is employed to solve the upper and lower level problems, respectively. Energy scheduling problems of the users are solved in a distributed manner which adds to the scalability of the approach. The proposed DR scheme is tested on a microgrid system adopted from the IEEE European low-voltage distribution network. Numerical experiments confirm the effectiveness of the proposed DR scheme compared to the benchmark pricing policies from the literature.