Demand Response Problem Research Articles

A neurodynamic algorithm to optimize residential demand response problem of plug-in electric vehicle

This paper proposes a nonconvex mixed-integer programming(MIP) electricity model of household load that is classified into base load and plug-in electric vehicle (PEV). The main objective of this model is to minimize the electricity cost of domestic user by adjusting the charging and discharging strategy of PEV according to the information of real-time price(RTP). In order to settle this problem, a neurodynamic algorithm which combines feedback neural network and inertial neural subnetwork is used. In this algorithm, the feedback neural networks are given as subnetworks to find a local optimal point and the inertial neural subnetwork can obtain different local optimal points by adjusting the inertial factor in global scope. The validity and the feasible of the proposed algorithm can be proven by performance evaluation of simulation results.

Stochastic Time-of-Use-Type Constraints for Uninterruptible Services

In this paper, a mixed integer linear formulation for problems considering time-of-use-type constraints for uninterruptible services is presented. Our work is motivated by demand response problems in power systems, in which certain devices must remain online once they are switched on, along with a certain number of periods. Classically, this kind of constraints are modeled as a summation over a rolling time window. This makes it difficult to consider this time-of-use parameter as uncertain. We propose an alternative formulation in which the time of use is on the right-hand side of a constraint instead on the limit of a summation. This allows applying existing stochastic optimization methodologies easily. An illustrative model for the optimal allocation of an uninterruptible load for the demand response problem supports the proposed formulation.

Distributed cooperative industrial demand response

Industrial demand response, whereby energy-intensive industrial processes shift their production to take advantage of time-varying electricity prices, is a popular topic of recent research. In a traditional demand response problem, the industrial process will have customers which set their own schedule for purchases of the industrial product, which the industrial process must meet. However, by introducing cooperation between an industrial process and its customers, additional cost savings are possible. In this work, we present an optimization framework for cooperative demand response, whereby the industrial process provides economic incentives to customers to shift their schedules to better align with the utility costs seen by the industrial process. As the industrial process and its customers are likely to be different entities which do not share their operational models, we propose a distributed algorithm for solving the problem using the alternate direction method of multipliers, which enables solving the problem with minimal information sharing. The ability of this algorithm to obtain solutions which improve upon the status quo is showcased through multiple case studies. We further demonstrate that cooperation provides the most benefit when the industrial process has limited ability to store its product and when the customer has a high degree of flexibility. We also show that this idea is practically applicable through a case study where an industrial process supplies hydrogen and nitrogen to an ammonia producer.

Novel Control Approaches for Demand Response With Real-Time Pricing Using Parallel and Distributed Consensus-Based ADMM

This paper studies the automated demand response (DR) problem in smart grids equipped with information and communication technology networks, where power generating and consuming units can exchange information as a multiagent system (MAS), and a real-time pricing (RTP) scheme is proposed. When the communication graph among agents is connected, a novel parallel and distributed consensus-based algorithm is proposed to derive an RTP scheme to facilitate DR, and when communication uncertainties exist, a robust consensus algorithm is proposed to cease the effect of uncertainties. Next, this paper proposes a novel control mechanism to tackle the problem of disconnected communication among agents, e.g., under cyber-attacks, by employing the so-called mixed communication-broadcast control architecture where the underlying ideas are twofold. First, each area in the grid associated with a connected subgraph is controlled by a MAS to guarantee the power balance and to reach consensus on the local electric price for that area. Second, a supervisory unit observes those local electric prices to calculate the global electric price for the whole grid and then broadcasts to all units so that they can properly adjust their output powers. Simulations are carried out on the IEEE 39-bus system to validate the proposed control mechanisms.

Learning-Based Demand Response for Privacy-Preserving Users

Demand response (DR), as a vital component of smart grid, plays an important role in shaping the load profiles in order to improve system reliability and efficiency. Incentive-based DR has been used in many DR programs by incentivizing customers to adapt their loads to supply availability. Note that users’ behavior patterns can be easily identified from fine-grained power consumption when interacting with the load serving entity (LSE), giving rise to serious privacy concerns. One common approach to address the privacy threats is to incorporate perturbations in users’ load measurements. Although it can protect the users’ privacy, yet the usage data modification would degrade the LSE's performance in achieving an optimal incentive strategy due to unknown characteristics of the augmented perturbations. In this paper, we cast the incentive-based DR problem as a stochastic Stackelberg game. To tackle the challenge induced by users’ privacy protection behaviors, we propose a two-timescale reinforcement learning algorithm to learn the optimal incentive strategy under users’ perturbed responses. The proposed algorithm computes the expected utility cost to mitigate the impacts of the random characteristics of the augmented perturbations and then updates the incentive strategy based on the perceived expected utility costs. We derive the conditions under which the proposed incentive scheme converges almost surely to an $\epsilon$ -optimal strategy. The efficacy of the proposed algorithm is demonstrated using extensive numerical simulation using real data.

A distributed game-theoretic demand response with multi-class appliance control in smart grid

We propose an event-triggered game-theoretic strategy for managing the power grids demand side, capable of responding to changes in consumer preferences or the price parameters coming from the wholesale market. The relationship between the retailer and the residential consumers is modeled as one-leader, N-follower Stackelberg game. We provide a detailed characterization of the household appliances to reflect the reality and improve the efficiency of the demand response (DR). Moreover, to consider all the appliances’ essentials, the consumer's objective function is formulated as a mixed integer non-linear program (MINLP), which, unlike conventional procedures, is solved via an integrated method. The proposed method consists of a day-ahead stage, in which the DR problem is solved for the next scheduling horizon, and a real-time stage which runs repeatedly to tackle the change in the parameters and adapt to the new condition. For any change in the grid, the consumers use the estimated optimal parameters (given by the original objective function) and develop another Stackelberg game based solution to maximize the satisfaction level. Given the appliances of multi-class nature, the proposed method is shown to be very tractable for ancillary services and reducing the mismatch between the renewable power generation and the load demand.

Design and Value Evaluation of Demand Response Based on Model Predictive Control

Demand response is one of the key technologies in energy management systems, and is defined as the changes in electricity usage of end-use consumers that occur as a result of changing the electricity price, the incentive, etc. In this paper, a design method for demand response is proposed based on model predictive control. Model predictive control is a control method using prediction by a mathematical model, which is used for controlling systems with constraints. Because constraints are considered in the design of demand response, it is appropriate to utilize model predictive control. First, the model-predictive-based demand response problem is formulated. By solving this problem, demand response is performed based on the actual power consumption. Next, constraints on the peak shift effect are imposed, and an event-triggered mechanism is introduced. Third, the effects of demand response are theoretically analyzed. Finally, the economic value of the proposed approach is evaluated by numerical simulations. The proposed approach provides us a fundamental result for the design of demand response and aggregators such as retailers.

Non-cooperative differential game based energy consumption control for dynamic demand response in smart grid

In this paper, we conduct research on the dynamic demand response problem in smart grid to control the energy consumption. The objective of the energy consumption control is constructed based on differential game, as the dynamic of each users' energy state in smart gird can be described based on a differential equation. Concept of electricity sharing is introduced to achieve load shift of main users from the high price hours to the low price hours. Nash equilibrium is given based on the Hamilton equation and the effectiveness of the proposed model is verified based on the numerical simulation results.

A Distributed Online Pricing Strategy for Demand Response Programs

We study a demand response problem from utility (also referred to as operator)'s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the utility to minimize its operational cost considering time-varying demand response targets and responses of consumers. We develop a joint online learning and pricing algorithm. In each time slot, the utility sends out a price signal to all consumers and estimates the cost functions of consumers based on their noisy responses. We measure the performance of our algorithm using regret analysis and show that our online algorithm achieves logarithmic regret with respect to the operating horizon. In addition, our algorithm employs linear regression to estimate the aggregate response of consumers, making it easy to implement in practice. Simulation experiments validate the theoretic results and show that the performance gap between our algorithm and the offline optimality decays quickly.

A survey on distributed optimisation approaches and applications in smart grids

Smart grid and smart metering technologies are transforming the utility industry and the customer experience in search of a new energy deal that supports a more collaborative, eco-friendly, stable, reliable and cost-efficient system as a whole. In order to unlock the full benefits, utilities need now to develop new technologies like distributed optimisation methods to excavate the latent value from the magnanimous data. This paper surveys recent advances of distributed optimisation and game algorithms with applications in power systems. In particular, this paper reviews distributed algorithms for model-based offline optimisation solution of dynamic economic dispatch problems, charging control problems for plug-in electric vehicles and risk-averse energy trading as well as model-free online algorithms for demand response problems.

Assuring explainability on demand response targeting via credit scoring

As data-driven innovation becomes a main trend in the energy sector, explainability of data-driven actions is becoming a major fairness issue for the residential applications, and it is expected to become a requirement for regulatory compliance. Explainability, however, often demands a sacrifice in prediction performance and affects the effectiveness of data-driven actions. In this study, we consider data-driven customer targeting in an incentive-based residential demand response program, and investigate the explainability-performance tradeoff when using simple-rule based, machine learning, and credit scoring methods. Credit scoring, that has been a popular solution in the finance discipline for over 60 years, is a scorecard based modeling method that can surely provide explainability. We first provide the detailed steps of applying credit scoring to the demand response problem. Then, we use a dataset of 14,525 households obtained from a real demand response program and analyze two prediction problems – participation prediction and behavior change prediction. The results show that credit scoring can achieve a comparable performance as the best-performing machine learning methods while providing full explainability. Our results suggest that credit scoring can be a promising explainability option for broader energy sector problems.

Privacy preserving distributed optimization using homomorphic encryption

This paper studies how a system operator and a set of agents securely execute a distributed projected gradient-based algorithm. In particular, each participant holds a set of problem coefficients and/or states whose values are private to the data owner. The concerned problem raises two questions: how to securely compute given functions; and which functions should be computed in the first place. For the first question, by using the techniques of homomorphic encryption, we propose novel algorithms which can achieve secure multiparty computation with perfect correctness. For the second question, we identify a class of functions which can be securely computed. The correctness and computational efficiency of the proposed algorithms are verified by two case studies of power systems, one on a demand response problem and the other on an optimal power flow problem.

Data-driven model predictive control using random forests for building energy optimization and climate control

Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study we implement and test DPC on real data from an off-grid house located in L’Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate robustness of our method to uncertainties both in real data acquisition and weather forecast.

Distributed real-time demand response for energy management scheduling in smart grid

Demand response is a promising technology to cope with the ever-increasing peak energy consumption in smart grid. For the smart home energy management system, it may choose to charge the battery when the electricity price is low, and discharge the battery to supply the home load when the electricity price is high. Under this circumstance, the temporally and spatially coupled constraints coexist in the energy demand, battery charging and discharging energy, and energy supply. The coupled constraints make the demand response problem more challenging than the existing ones and thus the existing distributed algorithms cannot be employed to find the optimal solution of the problem. In this paper, we focus on the real-time two-way communications between utility company and multiple residential users, and each user is regarded as a smart home energy management system. A distributed real-time algorithm is proposed to find the optimal energy management scheduling scheme for each user and utility company to maximize the social welfare. To overcome the obstacle brought by the temporally and spatially coupled constraints, dual decomposition technique is employed and the primal problem is decoupled into several independent subproblems which can be solved in a distributed way by each user and utility company without revealing or exchanging their private information. Simulation results are provided to demonstrate that the proposed distributed algorithm can bring potential benefits to the society.

A Stochastic Optimal Control Approach for Exploring Tradeoffs between Cost Savings and Battery Aging in Datacenter Demand Response

This brief paper optimizes power management for datacenters employing lithium-ion battery storage, with the specific goal of addressing the tradeoff between: 1) the cost saving achievable through the peak demand shaving and 2) the corresponding battery aging. To the best of the authors’ knowledge, this tradeoff has never been addressed using physics-based models of battery performance and degradation combined with stochastic models of datacenter demand. We build: 1) a Markov chain model of datacenter power demand; 2) a second-order model of battery diffusion/reaction dynamics; and 3) a physics-based model of battery aging via solid electrolyte interphase growth. Together, these models enable the solution of the battery health-conscious demand response problem via stochastic dynamic programming (SDP). A penalty function is used for enforcing a datacenter “power cap” within this SDP problem. By varying this power cap, we traverse the Pareto tradeoff between the cost savings due to demand response and battery health degradation.

An Energy Box in a Cloud-Based Architecture for Autonomous Demand Response of Prosumers and Prosumages

The interest in the implementation of demand response programs for domestic customers within the framework of smart grids is increasing, both from the point of view of scientific research and from the point of view of real applications through pilot projects. A fundamental element of any demand response program is the introduction at customer level of a device, generally named energy box, able to allow interaction between customers and the aggregator. This paper proposes two laboratory prototypes of a low-cost energy box, suitable for cloud-based architectures for autonomous demand response of prosumers and prosumages. Details on how these two prototypes have been designed and built are provided in the paper. Both prototypes are tested in the laboratory along with a demonstration panel of a residential unit, equipped with a real home automation system. Laboratory tests demonstrate the feasibility of the proposed prototypes and their capability in executing the customers’ loads scheduling returned by the solution of the demand-response problem. A personal computer and Matlab software implement the operation of the aggregator, i.e., the intermediary of the energy-integrated community constituted by the customers themselves, who participate in the demand response program.

Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making

Multi-agent system employs the functions of communication, coordination and cooperation among intelligent agents to help people judge and analyze complex phenomena that cannot be directly observed, and it has become an important tool for solving large-scale complex problems. The problem of demand response (DR) in electric power system is difficult to be modeled due to the complicated environment and continuously evolving subjects. Multi-agent system can simulate the operation mechanism of electric power system, thus playing an important role in solving the DR problems. In this study, based on multi-agent simulation, we establish a multi-agent model of residential power market and propose a satisfaction function of residential users about electricity price. We focus on the interaction process among all the agents of power supply side, selling side and demand side and conduct simulation to obtain the selection and decision-making of residential users on different electricity pricing schemes. The results show that multi-agent system is beneficial to analyze, simulate and solve the DR problem in power market. Also, the satisfaction function of residential users on electricity price can support power selling enterprise to better understand the intention of residential users when choosing electricity pricing schemes and participating in DR program.

Fully Distributed Demand Response Using the Adaptive Diffusion–Stackelberg Algorithm

In this paper, we consider the demand response problem in smart grid consisting of a retailer and multiple residential consumers, where the retailer determines consumers’ payments based on their power consumption profile. Our aim is to propose a fully distributed algorithm that is able to optimize the aggregate cost, utility, and retailer's profit simultaneously. To this end, we first formulate the consumer-side trend as a constrained convex optimization problem and propose a fully distributed adaptive diffusion algorithm to solve it. In addition, we design a one-leader $N$ -follower Stackelberg game to model interactions among the retailer and consumers. The proposed framework is able to continuously track the drifts resulting from the changes in the real-time pricing or the consumer preferences. Moreover, it is scalable and does not require network-wide information or rely on central controller. We provide comprehensive simulation results to show the effectiveness of the proposed framework.

Network Design in the Presence of a Link Jammer: a Zero-Sum Game Formulation

This paper considers the problem of designing a Network such that a set of dynamic rules converges as fast as possible to the Nash equilibrium in a class of repeated games, despite the attempt of a jammer to slow down the convergence by cutting a certain number of edges. Particularly we consider a class of quadratic games, motivated by the demand response problem in electricity markets. For a given network structure, a set of dynamic rules, based on approximate gradient decent is described. The convergence speed depends on the graph through a matrix which in turn depends on the graph Laplacian. The network design problem is formulated as a zero sum game between a network designer aiming to improve the convergence speed and a jammer who tries to deteriorate it. Simple heuristics for the designer and the jammer problems are proposed and a numerical example is presented.

Communication-Efficient Distributed Demand Response: A Randomized ADMM Approach

In this paper, we consider the distributed demand response (DDR) problem for achieving the real-time power balance in a neighborhood with a large number of load customers and renewable energy sources (RES). While most of the existing DDR schemes require iterative information exchange between the customers and the load aggregator through two-way communications, this paper studies the DDR schemes that rely on neighbor-wise communication between customers only. Such DDR schemes can be realized by low-cost wireless networks. To this end, we propose the use of a randomized alternating direction method of multipliers to develop a fully DDR algorithm. Notably, the proposed DDR algorithm is communication-efficient because it can yield promising power balance performance using a few times of neighbor-wise message exchanges. Moreover, the proposed DDR algorithm does not need synchronization between customers and is robust against random communication errors. For performing online demand response control, we combine the proposed DDR algorithm with the rolling-window-based model-predictive control method, and simple load and RES forecasting methods. By using real solar power data, we demonstrate via simulations that the proposed DDR algorithm improves the real-time power balance substantially and outperforms the existing DDR schemes that use the subgradient method for optimization.