Distributed Demand Response Research Articles

Optimal economic configuration by sharing hydrogen storage while considering distributed demand response in hydrogen-based renewable microgrid

With the issues of the greenhouse effect and environmental pollution globally, a high proportion of renewable energy has been integrated into microgrids. Its interconnection brings many challenges to the electric power industry. This paper proposes a new distributed response strategy through sharing hydrogen storage resources, aiming to solve the supply-demand imbalance in microgrids. First, the uneven power distribution from shared energy storage stations necessitates ensuring a fair power dispatch among users. Next, a microgrid demand response model with multi-constraint conditions and high penetration of wind and solar power is established, incorporating constraints like user power consumption, energy storage capacity, and charging/discharging power while considering differences in user electricity behaviors. Finally, the optimal economic configuration uses Lagrange duality theory to process the integrated models, including the demand response, hydrogen sharing, and power generation models, to make the optimization goals fairer. Results demonstrate that our strategy can adjust user electricity usage according to real-time supply-demand conditions, reducing users' total costs by 39.4 %. Compared with the existing configurations of sharing hydrogen storage configurations, our model uses more accurate utility functions, demonstrating better economic advantages, with users' total costs reduced by 4.89 % and revenue of the shared storage station increased by 4.02 %.

Deep learning for active detection of FDIAs to defend distributed demand response in smart grid

Deep learning for active detection of FDIAs to defend distributed demand response in smart grid

Distributed demand response charging control of multiple plug‐in electric vehicle clusters

AbstractThis paper is devoted to the distributed demand response (DR) charging control of plug‐in electric vehicles (PEVs) for frequency regulation in power systems in terms of load following service. Specifically, PEVs are divided into different clusters according to their parking place under different energy management systems. Each EV cluster is modelled by a transport‐based load aggregate model with the input being the charging rate, and the output is the aggregate power. Based on the aggregate charging control model, a novel dynamic real‐time distributed pinning control algorithm is proposed to coordinate the charging rates such that the aggregate charging power of PEVs can follow a given reference power trajectory. The theoretical analysis shows that if the reference power profile is in the trackable area of all PEVs' charging power and the ramping rate is restrained by a predefined bounded constraint, then the demand response charging tracking control is solvable. Finally, simulation results on an EV system with twelve PEV clusters are presented to show the effectiveness of the proposed demand response control algorithm.

Privacy-preserving demand response of aggregated residential load

The randomness, dispersion, and small capacity of residential load make it difficult to participate in incentive-based demand response. Meanwhile, the rapid development of Internet of things makes it possible to sense and regulate power-consuming behavior of most residents. Load aggregator (LA) has become a feasible scheme as an intermediate form in this situation. It improves the bargaining power of residential load in the market, making it change from price-taker to price-maker, to obtain more profit. However, privacy disclosure is the primary concern when residents directly communicate with LA. A distributed demand response (DR) approach for aggregated residential load is proposed to maximize the benefit of LA while preserving the privacy of residents. Based on a mixed-integer model, a two-layer framework between LA and residents is developed to solve the model above based on the sharing alternating direction method of multipliers. The privacy of residents is preserved by interacting insensitive information between the two layers. The proposed approach is deployed and tested in a real-world residential building with 27 apartments. The results demonstrate that this scheme can realize effective participation of large scale residential load in incentive-based DR on the premise of preserving privacy, which verifies the feasibility and effectiveness of the scheme.

Multi-level distributed demand response study for a multi-park integrated energy system

Current studies of integrated demand response (IDR) across multiple campuses often use centralized, unified scheduling with individual campuses as the object of analysis, which ignores the independent autonomy of users within the park. To this end, this paper proposes a multi-level distributed demand response model for a multi-park integrated energy system, which is solved using a combination of the particle swarm optimization algorithm and mixed integer linear programming software. First, based on the existing two-part tariff, an energy management system is introduced to construct a park integrated demand response market based on a noncooperative game. Second, load aggregators are used as a link to form an optimized alliance of multiple parks, and the overall economy of the coalition is optimized through a cooperative game of multiple parks. The model allows for coordination and complementarity based on zonal autonomy, maximizing economic efficiency and exploring the demand response capability of users. Finally, an arithmetic simulation is carried out with a scenario of a day-ahead electricity peaking auxiliary service market, and the results verify the feasibility and economics of the designed model in a scenario where multiple parks participate in integrated demand response.

A Stochastic Approach to Integrating Electrical Thermal Storage in Distributed Demand Response for Nordic Communities With Wind Power Generation

Demand response and distributed energy storage play a crucial role in improving the efficiency and reliability of electric grids. This paper describes a strategy for optimally integrating distributed energy storage units within a forward market to address space heating demand under a Stackelberg game in isolated microgrids. The proposed strategy performs distributed management in an offline fashion through proximal decomposition methods. It leverages stochastic programming to consider user flexibility degree and wind power generation uncertainties. Also, flexibility for demand response is realized through electric thermal storage (ETS). The performance of the proposed strategy is evaluated via simulation studies carried out through a case study in Kuujjuaq. Ten residential agents compose the demand side, each with flexibility levels and economic preferences. Economic benefits are evaluated on both sides across different ETS acceptance levels. The simulation results demonstrate significant benefits for the coordinator and customers. The proposed strategy reveals that ETS, in the presence of dynamic tariffs, reduces diesel consumption, maximizes renewable production and reduces grid stress.

Population games with replicator dynamics under event-triggered payoff provider and a demand response application

We consider a large population of decision makers that choose their evolutionary strategies based on simple pairwise imitation rules. We describe such a dynamic process by the replicator dynamics. Differently from the available literature, where the payoffs signals are assumed to be updated continuously, we consider a more realistic scenario where they are updated occasionally. Our main technical contribution is to devise two event-triggered communication schemes with asymptotic convergence guarantees to a Nash equilibrium. Finally, we show how our proposed approach is applicable as an efficient distributed demand response mechanism.

Optimum Power Flow with Respect to the Capacitor Location and Size in Distribution Network

In the past few decades, there has been increasing recognition of the importance of optimum power flow (OPF) studies in the context of economic analyses of power systems. There is a need for power system development to maximize efficiency by emphasizing cost and power losses for smart grids to operate effectively in the current situation. This study aims to develop an optimal capacitor bank allocation schedule that minimizes power losses in the distribution networks under equality constraints. This will be achieved by integrating the loss factor and voltage stability into a new approach to determine where the capacitor banks should be located. It aims to reduce the operating costs of power systems and maximize efficiency by applying an optimization model for economic dispatch, which considers distributed power generation and demand response. The NSGA-II optimization algorithm was used in this study to determine the optimal size and location of the capacitor bank. A NSGA-II solves this problem by minimizing cost and power losses while determining the best operating strategy. We used an IEEE 26-bus distribution system to test the proposed method with every possible generation change. Comparing the power flow analysis with/without capacitor optimization showed that the operation optimization model of OPF with NSGA-II can reduce operation costs and improve the power system.

Data-oriented distributed overall optimization for large-scale HVAC systems with dynamic supply capability and distributed demand response

Operational optimization of heating, ventilation, and air-conditioning (HVAC) systems is crucial to achieving economic and environmental benefits. However, owing to the dynamic supply capability (DSC) and distributed demand response (DDR), large-scale HVAC systems that integrate multiple distributed sources with demanders are increasingly being developed to resemble distributed energy systems (DESs); thus, the traditional centralized strategies are now facing unprecedented challenges. With the application of wireless communication and sensor network technology, distributed schemes that allow the modular integration of an arbitrary number of suppliers and demanders have attached widespread interest of researchers. However, existing distributed optimization schemes that focus on either multi-zone temperature or equipment group efficiency are unilateral and non-interfering. In this work, we developed a data-oriented distributed overall optimization strategy for large-scale HVAC systems with DSC and DDR. We first suggested an agent-based distributed overall optimization framework that defines the roles of participants in distributed optimization and clarifies their duties, mechanisms, and relationships with each other, thus bridging the supply-side and demand-side decisions. Then, we exploited state-of-the-art data-driven models and designed a bi-level optimization algorithm, where the heuristic algorithm works as the lower-level scheme and the Nash-optimization algorithm acts as the upper-level scheme, to implement the distributed overall optimization. Last, a hardware-in-the-loop simulation was performed, and time-series data were collected to validate our proposed optimization strategy. Compared with centralized strategies, it can obtain better operational performance by paying a lower computational cost. This work provides a reference for optimizing increasingly complex HVAC systems.

Distributed response strategy of electric heating loads based on temperature queue sorting

In order to eliminate the adverse impact of the rapid growth of electric heating loads (EHLs) on the safe and stable operation of the power grid under the background of the development of clean heating, a distributed response strategy based on demand response technology is proposed. Based on the thermodynamic dynamic model of EHLs, its demand response capability is analyzed, and then the processes of EHLs centralized response strategy and temperature queue sorting distributed response strategy are compared. The core advantage of the distributed response strategy of temperature queue sorting is that the distributed control of EHLs is realized by setting the response threshold and recovery threshold for the two indicators of frequency and temperature, which not only takes into account the user's comfort, but also ensures the significant improvement of frequency drop under fault. A two zone test system is established, and the simulation verifies the effectiveness of the proposed response strategy. The results show that the EHLs temperature queue sorting distributed demand response strategy can effectively suppress the sharp decline of frequency in case of system failure, and avoid the impact of a large number of EHLs responses on the power grid, which plays a positive role in the safe and stable operation of the power grid.

Distributed Demand Response Management for a Virtually Connected Community With Solar Power

With the high proliferation of solar power, curtailments and higher capacity reserves are required for a reliable power system operation. However, system operators can tap into demand flexibility to maintain reliability affordably. Temperature-controlled loads (TCL) have higher flexibility in demand response due to their frequency of operation, power rating, and tolerance in the desired operating region. In this study, a TCL demand flexibility quantification is presented using temperature measurements and consumer preferences, and predictions of TCL demand flexibility and solar power generation are used to improve demand response (DR) reliability. The utility can leverage predictions to issue DR requests considering resource adequacy and operational costs. Consumers are formed as a virtually connected community, and an aggregator facilitates the utility in providing situational intelligence and distributing DR requests among consumers. A distributed DR management framework is proposed based on demand flexibility to (a) simplify the optimization and (b) improve optimality. Typical results show power consumption reduction during peak reduction and emergency DR requests and power consumption with low variability during capacity firming requests compared to individual thermostat controls. Two indices to measure DR reliability and consumer comfort are defined, and results are presented for different DR requests.

A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid

With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9 % recall, 100% F1, 100% precision, 99.21 % training accuracy, and 99.70% testing accuracy.)

Distributed Demand Response with Multi-Type Air Conditioner Integration: A Numerical Case

Green and clean energy are increasingly becoming the top priority of global energy development, in which demand response plays an important role to improve the consumption of green energy generation and the process of energy substitution. The cluster of air-conditioning loads has great potential to participate in demand response, while different control methods and operating characteristics corresponding to different air-conditioning types will have different effects on the results of demand response. Based on the research on the load features of fixed-frequency and variable-frequency air conditioners and the characteristics of participation in demand response, through a distributed response strategy of slacked consistency, the influence of different control methods and different proportions of two types of air conditioning loads is compared and analysed through simulation cases. The results show that when the number of response users is the same, the schedulable potential of air-conditioning cluster is positively correlated with the proportion of fixed-frequency air-conditioning in the cluster.

A Weak-Consistency–Oriented Collaborative Strategy for Large-Scale Distributed Demand Response

Large-scale distributed demand response is a hotspot in the development of power systems, which is of much significance in accelerating the consumption of new energy power generation and the process of clean energy substitution. However, the rigorous distributed algorithms utilized in current research studies are mostly very complicated for the large-scale demand response, requiring high quality of information systems. Considering the electrical features of power systems, a weak-consistency–oriented collaborative strategy is proposed for the practical implementation of the large-scale distributed demand response in this study. First, the basic conditions and objectives of demand response are explored from the view of system operators, and the challenges of large-scale demand response are discussed and furthermore modelled with a simplification based on the power system characteristics, including uncertainties and fluctuations. Then, a weakly consistent distributed strategy for demand response is proposed based on the Paxos distributed algorithm, where the information transmission is redesigned based on the electrical features to achieve better error tolerance. Using case studies with different information transmission error rates and other conditions, the proposed strategy is demonstrated to be an effective solution for the large-scale distributed demand response implementation, with a robust response capability under even remarkable transmission errors. By integrating the proposed strategy, the requirement for the large-scale distributed systems, especially the information systems, is highly eased, leading to the acceleration of the practical demand response implementation.

Data-oriented distributed demand response optimization with global inequality constraints based on multi-agent system

This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader’s strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play.

A CVaR-Robust-Based Multi-Objective Optimization Model for Energy Hub Considering Uncertainty and E-Fuel Energy Storage in Energy and Reserve Markets

The increasing demand for energy carriers has expanded the use of energy hubs that employ distributed demand response programs to improve power system reliability and efficiency. Moreover, the unstable behavior of renewable resources, as well as the indeterminate electrical and thermal demands, create major problems for energy hub operation. Inspired by this, this paper presents a day-ahead scheduling framework for energy hubs (EH) in energy and reserve markets considering two main objectives of economy and pollution emission. The studied energy hub consists of a novel hybrid energy storage facility based on a fuel cell, wind power, photovoltaic energy, and a particular fuel cell unit in the presence of elastic demand. This multi-component system participates in energy and reserve markets as a single entity to optimize energy hub operation. The proposed method also models the uncertainty of wind speed, photovoltaic irradiance, and load using the Mont-Carlo method. The energy hub risk level is analyzed using the conditional value at risk (CVaR) approach to increase the EH operation and efficiency. The proposed multi-objective energy hub model is solved using the MINLP method in General Algebraic Modeling System (GAMS) to minimize operation cost and pollution emission. Finally, to prove the effectiveness of adding a new E-fuel energy storage system and considering uncertainties on energy hub operation, the proposed method is compared with other reported models.

Distributed demand response market model for facilitating wind power integration

To cope with wind power uncertainty, balancing authorities are required to procure adequate ancillary services (ASs) with the aim of maintaining the security of the power system operation. The transmission system operator (TSO) is responsible for maintaining the balance between supply and demand in delivery hours. Besides the generating units, demand response (DR) has the potential capabilities to be considered as a source of AS. The demand‐side AS can be used both locally (by the local entities in distribution networks) and system‐wide (by the TSO). However, the optimal coordination between the local and global beneficiaries is a challenging task. This study proposes a distributed DR market model, in which the DR is traded as a public good among the providers and beneficiaries through the local DR markets. The local DR markets can be run in each load bus to trade the DR provided by retail customers connected to that bus with the buyers. To include the interactions between the energy/reserve market and the local DR markets, a bi‐level programming model is proposed. The bi‐level problem is translated into a single‐level mixed‐integer linear programming problem using the duality theorem. The proposed model is verified by simple and realistic case studies.

Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid.

In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings.

Neural-network-based Lagrange multiplier selection for distributed demand response in smart grid

As a general difficult problem, the slow convergence of existing distributed demand response methods greatly hinders the reliable applications in smart grid. To overcome this problem, this paper proposes a new distributed method, namely the neural-network-based Lagrange multiplier selection (NN-LMS), to prominently reduce the iterations and avoid an oscillation. The key improvement lies in the forecast strategy of a load serving entity (LSE), who applies a specially designed neural network to capture the users’ price response features. A novel Lagrange multiplier selection model, the NN-LMS model, is formulated to optimize the iterative step sizes. This complex model is then solved by a two-stage NN-LMS algorithm, containing the interval bisection method and improved sensitivity method in the first and second stages, respectively. In addition, the data selection, batch normalization and additional network are applied to boost the performance of neural networks. Case studies validate the optimality, improved convergence and numerical stability of the proposed method, and demonstrate its great potential in distributed demand response and other smart grid applications.

Distributed Demand Response for Multienergy Residential Communities With Incomplete Information

This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes’ behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes’ computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.