Flexible Demand Resources Research Articles

Pattern-driven behaviour for demand-side management: An analysis of appliance use

Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities.

Graph Deep-Learning-Based Retail Dynamic Pricing for Demand Response

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily en-ergy consumption behaviors. It helps manage flexible de-mand response resources on peak load. However, it is insuf-ficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mecha-nism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers energy consumption behaviors under different prices. Then, to avoid unfair eval-uation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumers time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superior-ity of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

A Market Clearing Mechanism Considering Primary Frequency Response Rate

Prior-art market mechanism designs in the literature do not well distinguish the primary frequency reserve resources with different response rates. This letter proposes a performance ratio that quantifies the relative effectiveness of generators and flexible demand resources in frequency regulation considering their response rates. The performance ratio is incorporated into a real-time market clearing model to optimize both upward and downward primary frequency reserve resources. The proposed method is validated based on the IEEE 118 bus system.

Distributed Consensus-Based Coordination of Flexible Demand and Energy Storage Resources

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.

Privacy preservation method for MIQP-based energy management problem: A cloud-edge framework

The increasing development of information and communication technologies in power grid makes it possible to optimize the energy management of numerous physical users with cloud-based service, while users’ concerns about privacy security have attracted increasingly more attention. To attack this challenge, this paper proposes the information masking (IM) method for the energy management problem in the form of mixed-integer quadratic programming (MIQP). Additionally, the feasibility and optimality of the recovered solution in the IM of MIQP is proven and given in the form of two lemmas. Next, the implementation mechanism of IM is designed based on a cloud-edge framework. Furthermore, the general requirements of the IM of MIQP are elaborated with respect to privacy security and computational cost to better accommodate practical applications. Finally, the optimal schedule of microgrid with on-site generators and flexible demand resources is modelled and simulated to demonstrate the feasibility and effectiveness of the proposed methodology.

Special Issue on Advanced Approaches and Applications for Electric Vehicle Charging Demand Management

The papers in this special issue focus on advanced approaches and applications for electric vehicle charging. Due to the concern of carbon emission, pollution, and oil depletion, transportation electrification has drawn more and more attention in both industrial and academic sectors. As one type of rapidly increasing power demands, electric vehicle (EV) charging loads have unique characteristics, as they are typically equipped with battery storage and moving capability. With those energy storage equipment, EV charging loads obtain flexibility in the temporal dimension by strategically schedule their charging and discharging states in different time periods. Meanwhile, with the mobility feature of vehicles, EV charging demands get flexibility in the spatial dimension by optimally finding their charging and discharging locations. Considering the unique characteristics of charging behaviors, EV charging demands can be utilized as flexible demand resources to enhance the operation and planning efficiency for both power system and transportation system.

Distributed coordination of deferrable loads: A real-time market with self-fulfilling forecasts

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved, scheduling them is a complex task. This paper proposes a forecast-mediated real-time market-based control approach (F-MBC) for cost minimizing coordination of uninterruptible time-shiftable (i.e. deferrable) loads. F-MBC is scalable, privacy preserving, and usable by device agents with small computational power. Moreover, F-MBC is proven to overcome the challenge of mutually conflicting decisions from equivalent devices. Simulations in a simplified but challenging case study show that F-MBC produces near-optimal behaviour over multiple time-steps.

Harnessing demand-side management benefit towards achieving a 100% renewable energy microgrid

Optimal sizing with energy management strategy as a transition pathway towards a sustainable 100% renewable energy-based microgrid is investigated in this paper. Due to the challenges of intermittency of renewable energy, microgrid operations are complicated. Hence, in order to overcome some of the challenges facing microgrid planning and operations, optimal capacity sizing incorporated with energy management strategy considering time-ahead generation prediction is proposed. The system model consists of wind turbine (WT), solar photovoltaic (PV) and battery energy storage system (BESS). The generation forecasting output is used to reschedule the flexible demand resources (FDR) to reduce the mismatch between power demand and supply, and optimal sizing of components is performed jointly to determine the optimal capacity values of the PV, WT, and BESS for minimal investment costs. The optimization results for the scenarios with and without load shifting effects of FDRs are determined and analyzed for the case study. From the results obtained, the application of demand scheduling program using the generation forecasting outputs resulted in a cost-saving of 12.41%. The forecasting model is implemented using a random forest algorithm on python platform and the mixed-integer linear program on MATLAB® environment is used to model and solve the capacity sizing problem.

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand resources becomes one of the most significant challenges for promoting incentive-based DSM under microgrid environments. In this paper, a flexible demand resource pricing scheme is proposed. Instead of using the utility function of end users like most existing literatures, the economic benefit of flexible demand resources is evaluated by the operation performance enhancement of microgrid and correspondingly the resource is priced based on a benefit sharing approach. An iteration-based chance-constrained method is established to calculate the economic benefit and shared compensation for demand resource providers. Meanwhile, the financial risks for the microgrid operator due to uncertain factors are mitigated by the chance-constrained criterion. The proposed scheme is examined by an experimental microgrid to illustrate its effectiveness.

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population

Residential air conditionings (RACs) can be seen as one of the important flexible demand resources controlled to provide balance services for the power system due to their spatial thermal storage feature. However, such control on RACs will bring out the negative impact on the comfort level of residents inevitably, affecting their proactivity in demand response (DR). So, a novel two-stage control model of large-scale RACs is proposed to participate in DR considering different comfort sensitivity of various residents. The comfort sensitivity index is developed in terms of daily operation time and daily power consumption per area of RACs, classified by K-means clustering. Besides, the DR potential of RACs is established with the adjusted temperature setpoint, where the temperature setpoint is determined by the comfort sensitivity index. Moreover, a two-stage control model of large-scale RACs is proposed comprised of load response and recovery, where the corresponding control strategies at each stage are proposed. Simulations of the proposed method have been performed on residential 20,000 RACs to validate its effectiveness in Changzhou, China. The outcome reveals that the strategies could maximize the DR potential of RACs and ensure the stable recovery of RACs under the premise of meeting the comfort demand of differentiated population.

Optimal Energy Trading for Renewable Energy Integrated Building Microgrids Containing Electric Vehicles and Energy Storage Batteries

The increasing penetration of renewable energy resources and volatility of energy prices cause huge challenges in planning and regulating energy generation, transport, and distribution. A possible solution can be a paradigm change of employing control actions from the demand side in addition to the conventional generation control. To realize such shifts, the primary stage should be a proper and robust analysis of the energy flexibility on the demand side. Recently, demand side control in buildings has become a major research issue because buildings share a substantial portion of the total electricity consumption. The increasing use of controllable devices in buildings combined with the advent of smart metering system has paved the way to exploit the potential flexibility of managing the energy generation and demand of buildings for optimal energy trading. In this paper, we investigate the benefits of demand resources in buildings for optimal energy trading in day-ahead and real-time energy markets. The building flexible demand resources considered are electric vehicles and batteries. The paper examines the combined optimization of EVs and batteries in the day-ahead and regulation electricity markets with the objective of maximizing the total profit of the building microgrid. It takes EVs driving pattern into consideration. The major contribution of the paper is the exploitation of the energy flexibility of buildings using EVs as dynamic energy storage device and batteries as manageable demand facility. The devised optimization problem is formulated as a double-stage mixed-integer linear programming (MILP) problem, and solved using the CPLEX solver. Several numerical results are presented to validate the effectiveness of the devised optimization framework using actual data of building electricity demand and local renewable generation in the Otaniemi area of Espoo, Finland. We demonstrate that the proposed optimization solution can achieve considerable increase in profit, reduce renewable energy curtailment and decrease power demand in peak hours, compared to uncontrolled or non-optimized operation.

Nodal market power assessment of flexible demand resources

With the incorporation of higher shares of intermittent renewable energies (RES), more flexible resources are required in power systems to keep load balance. Under some extreme circumstances, the flexible demand resources (FDRs) may have the potential to dominate and obtain excess benefits, preventing other FDRs from participating in the electricity markets. Therefore, it is of great significance to identify the key FDR market power locations and implement some corresponding regulations. However, the relevant researches in power systems focused on the supply side, rather than the demand side. In this paper, a novel nodal market power analysis method is proposed to evaluate the potential influence of FDRs on electricity markets. Firstly, a multi-state model is established to present the multiple power system operation states including the random failures of system components. Then, the nodal market power assessment model is established under each specific state and new indices are proposed to evaluate the nodal market power of FDRs quantitatively. Furthermore, the key FDR nodes in demand side with stronger power in capturing excess revenue are identified. The 24-bus IEEE Reliability Test System is modified to demonstrate the feasibility of the proposed method. The numerical results of the proposed method are capable to display the existence of market power in demand side, and provide some valuable guidance for classification and operation of electricity markets.

Quantifying demand flexibility based on structural thermal storage and comfort management of non-residential buildings: A comparison between hot and cold climate zones

Recently, demand flexibility has been highlighted as a promising distributed resource from the customer side, especially from industrial customers like commercial buildings, capable of providing grid support services. However, the quantification of demand flexibility is a complex process that requires a methodology including the requirements of both the grid operators and the customers. This paper proposes a novel approach to quantify the available demand flexibility of individual buildings, while taking into account the underlying building energy physics. The proposed approach constructs on the operational flexibility concept from the power systems, and extends it to include a comfort domain, identifying different flexibility parameters with the aim of giving a better insight into the flexibility potential of commercial buildings. This method includes a development of building energy simulations to assess the effects of weather variations, construction types, and comfort constrains on demand flexibility. The proposed quantification method has been validated using 15 different office building models and two different climate zones, i.e., the Netherlands and Texas, US. The results presented in this paper suggest that buildings located in a hot climate could offer higher flexibility potential during shorter time ranges, while buildings in a cold climate could offer lower flexibility potential but during longer time ranges. Determining these differences could potentially facilitate the dispatch of flexible demand resources, to assess their real potential, and to schedule demand flexibility between stakeholders.

Multi-state time-varying reliability evaluation of smart grid with flexible demand resources utilizing Lz transform

With the expanding proportion of renewable energy generation and development of smart grid technologies, flexible demand resources (FDRs) have been utilized as an approach to accommodating renewable energies. However, multiple uncertainties of FDRs may influence reliable and secure operation of smart grid. Multi-state reliability models for a single FDR and aggregating FDRs have been proposed in this paper with regard to responsive abilities for FDRs and random failures for both FDR devices and information system. The proposed reliability evaluation technique is based on Lz transform method which can formulate time-varying reliability indices. A modified IEEE-RTS has been utilized as an illustration of the proposed technique.

Special Issue on Smart Grid and Emerging Technology Integration

Special Issue on Smart Grid and Emerging Technology Integration