Demand Response Providers Research Articles

Stochastic multi-objective optimization to design optimal transactive pricing for dynamic demand response programs: A bi-level fuzzy approach

This paper proposes a stochastic multi-objective optimization framework to design real-time pricing for transactive energy. The energy transaction among the load-serving entity and customers is formulated as a bi-level optimization in which load-serving entity acts as the leader in the upper-level of optimization aiming to profit and reserve maximization. To make the interaction between opposite objectives, the modified fuzzy method is employed. The load-serving entity takes part in the wholesale market and purchases energy from the wholesale market and sells it to the customers. The load-serving entity itself possesses some dispatchable resources and it can also buy energy from privately-owned renewable resources. On the other hand, demand response providers are buying energy from the load-serving entity on behalf of consumers. The demand response providers are the followers of the proposed model and seek to maximize the profit of their customers. The communication between LSE and customers can be provided by the energy internet. Using the energy internet, the LSE controls and monitors the behavior of costumers to design real-time prices. The proposed model is tested on a standard case study, and the results show that the reservation and energy not supplied have been improved as 12% and 43.8%, respectively.

Industrial demand response: How network tariffs and regulation (do not) impact flexibility provision in electricity markets and reserves

Incentives for industrial loads to provide demand response on day-ahead and reserve markets are affected both by network tariffs, as well as regulations on the provision of flexibility in different markets. This paper uses a numerical model of the chlor-alkali process with a storable intermediate good to investigate how these factors affect the provision of demand response in these markets. We also model the effect of network tariffs and regulation on endogenous investment into process excess capacities, which are needed to provide load shifting. We find that fixed network tariffs based on peak-demand (demand charges) can be detrimental for the provision of demand response, especially on new investments in process capacity. For existing excess capacities, only high network tariffs inhibit demand response by limiting the optimal peak load below its physical limit. Marketing flexibility on the day-ahead market and in the reserves are substitutes for each other. The choice where to market flexibility is affected both by fixed peak-demand network tariffs and existing excess capacities. For endogenous investments, there are synergies between primary reserve participation and day-ahead flexibility provision, with the combination leading to increased capacity investments. In contrast, so-called interruptible load reserves, regular payments to industrial loads to be able to reduce electricity consumption at any point in time, incentivize a flat demand level. Consequently, such reserve markets reduce investments into additional flexibility capacities and often crowd out active participation in other markets.

The economics of costly demand response

The Ontario Energy Board recently heard an application from incumbent demand response providers claiming that changes to the system operator’s demand response-only capacity auction to allow certain generators to participate were unjustly discriminatory. The claim was based on costs borne by demand responders when curtailed that would not be recovered without energy payments. Using basic economic theory this paper analyzes the implications of variable costs of demand response and how they should best be handled to preserve economic efficiency of dispatch.

Aggregated Model of Data Network for the Provision of Demand Response in Generation and Transmission Expansion Planning

A two-stage Internet data center (IDC)-considered framework in generation and transmission expansion planning (GTEP) (referred to as IGTEP) is proposed to make full use of IDCs’ spatial and chronological load regulation potentials for demand response. Stage one aggregates the data network (DN) to match a transmission network structure. First, IDC load is modeled based on the DN configuration. Second, the aggregated DN (ADN) is proposed to model DN with aggregated front-end servers (FSs) and data flows, where an intuitively accurate mathematical relationship among IDCs is developed. Third, the virtual power network (VPN) is proposed to provide a model for ADN with electrical variables, where the coupling of DN and power network (PN) is proposed to develop a linear aggregated IDC load model. Stage two coordinates IDC DR and GTEP, in which the load coupling among multiple IDCs is managed by VPN and the corresponding IDC load model is compatible with the conventional GTEP load. Simulation results verify the efficiency of the proposed VPN and IDC load model. Simulation results also show that IDC DR may reduce the total IGTEP cost significantly, which implies that DN can play a role equivalent to power lines and generators in power system planning and operations.

Coordinating Demand Response Aggregation With LV Network Operational Constraints

High shares of renewable energy resources are increasing the value of using demand response (DR) mechanisms to enhance the resiliency and efficiency of power system operation. In this context, residential DR is expected to become an increasingly important asset. In order to exploit this resource, DR aggregators are expected to combine the capabilities of a large group of householders and participate, as a single provider, in the electricity market. In doing so, it is imperative for aggregators to consider the operational constraints of the local network. Otherwise, the quality of the electricity service can be jeopardized and the true DR potential overestimated. This paper presents a novel methodology that integrates DR aggregators with distribution network operators for a secure and efficient scheduling and real-time operation of DR in residential radial feeders. The method is evaluated on a real unbalanced feeder where DR provision (from a mix of technologies) is coordinated with the network steady-state thermal and voltage limits. In this study, with the corresponding data seasonality and controllable devices, it is shown that complying with network operational constraints can impose, at certain times, a significant limit to the allocation of DR.

ElectroBlocks: A blockchain‐based energy trading scheme for smart grid systems

SummarySmart grid systems are widely used across the world for providing demand response management between users and service providers. In most of the energy distributions scenarios, the traditional grid systems use the centralized architecture, which results in large transmission losses and high overheads during power generation. Moreover, owing to the presence of intruders or attackers, there may be a mismatch between demand and supply between utility centers (suppliers) and end users. Thus, there is a need for an automated energy exchange to provide secure and reliable energy trading between users and suppliers. We found, from the existing literature, that blockchain can be an effective solution to handle the aforementioned issues. Motivated by these facts, we propose a blockchain‐based smart energy trading scheme, ElectroBlocks, which provides efficient mechanisms for secure energy exchanges between users and service providers. In ElectroBlocks, nodes in the network validate the transaction using two algorithms that are cost aware and store aware. The cost‐aware algorithm locates the nearest node that can supply the energy, whereas the store‐aware algorithm ensures that the energy requests go to the node with the lowest storage space. We evaluated the performance of the ElectroBlocks using performance metrics such as mining delay, network exchanges, and storage energy. The simulation results obtained demonstrate that ElectroBlocks maintains a secure trade‐off between users and service providers when using the proposed cost‐aware and store‐aware algorithms.

Air-conditioning demand response resource assessment for Australia

It is widely acknowledged that demand response must play an increasingly important role in future electricity markets. This is as much due to increasing demand for air-conditioning as it is due to the changing sources of generation. In the National Electricity Market (NEM) in Australia, for just 3% of the year demand increases by 20%, or the equivalent of the generation from the two largest coal fired power stations combined. Over smaller regions and time intervals demand spikes are larger. Air-conditioning demand response takes advantage of a buildings inherent thermal storage to reduce electricity demand at peak times with no discernible impact on the indoor operating environment. Here we estimate the existing potential of commercial and residential air-conditioning systems to provide demand response across Australia. We use a top-down approach based on disaggregation of electricity substation half-hourly data. Results are that peak (i.e. 99.5th percentile) demand on the NEM could be reduced by up to 5.8% or 1.2 GW with the time of day at which the peak occurs delayed by approximately 2 hours. Based on the timing of the available capacity, both residential and commercial buildings are suited to providing air-conditioning demand response.

Separating Feeder Demand Into Components Using Substation, Feeder, and Smart Meter Measurements

Real-time, feeder-level energy disaggregation seeks to separate the measured total demand of a distribution feeder into components (e.g., into the aggregate demand-responsive load and remaining demand on the feeder, or into the aggregate generation and demand on the feeder). It can benefit distribution system operators and demand response providers by providing real-time information about balancing reserve requirements or the aggregate demand-responsive load, among other applications. In this work, we develop a feeder-level energy disaggregation algorithm that uses measurements from multiple sources that are available on different timescales. The algorithm is based on online learning and uses sensor fusion to incorporate output equations associated with disparate active and reactive power flow measurements, complex bus voltage measurements, and residential smart meter measurements. The algorithm also uses distribution substation measurements. Case studies simulate a three-phase distribution feeder model with unbalanced loads, where the active and reactive demand of the loads are modeled as a function of voltage and temperature. These models are parameterized using real-world active power demand data at each time-step. Results indicate that disaggregating three demand components is possible using feeder head active power measurements, that including feeder head reactive power measurements improves disaggregation accuracy by 32% on average, and that including intermittent real-time smart meter measurements further improves disaggregation accuracy.

On the Sensitivity of Local Flexibility Markets to Forecast Error: A Bi-Level Optimization Approach

The large-scale integration of intermittent distributed energy resources has led to increased uncertainty in the planning and operation of distribution networks. The optimal flexibility dispatch is a recently introduced, power flow-based method that a distribution system operator can use to effectively determine the amount of flexibility it needs to procure from the controllable resources available on the demand side. However, the drawback of this method is that the optimal flexibility dispatch is inexact due to the relaxation error inherent in the second-order cone formulation. In this paper we propose a novel bi-level optimization problem, where the upper level problem seeks to minimize the relaxation error and the lower level solves the earlier introduced convex second-order cone optimal flexibility dispatch (SOC-OFD) problem. To make the problem tractable, we introduce an innovative reformulation to recast the bi-level problem as a non-linear, single level optimization problem which results in no loss of accuracy. We subsequently investigate the sensitivity of the optimal flexibility schedules and the locational flexibility prices with respect to uncertainty in load forecast and flexibility ranges of the demand response providers which are input parameters to the problem. The sensitivity analysis is performed based on the perturbed Karush–Kuhn–Tucker (KKT) conditions. We investigate the feasibility and scalability of the proposed method in three case studies of standardized 9-bus, 30-bus, and 300-bus test systems. Simulation results in terms of local flexibility prices are interpreted in economic terms and show the effectiveness of the proposed approach.

Prediction and assessment of demand response potential with coupon incentives in highly renewable power systems

Demand Response (DR) provides both operational and financial benefits to a variety of stakeholders in the power system. For example, in the deregulated market operated by the Electric Reliability Council of Texas (ERCOT), load serving entities (LSEs) usually purchase electricity from the wholesale market (either in day-ahead or real-time market) and sign fixed retail price contracts with their end-consumers. Therefore, incentivizing end-consumers’ load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market. As the first-of-its-kind implementation of Coupon Incentive-based Demand Response (CIDR), the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements, individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets, as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts. A number of methodologies are developed for this special type of DR program including price/baseline prediction, individualized target setting and a lottery mechanism. This paper summarizes the methodologies, design, critical findings, as well as the potential generalization of such an experiment. Comparison of the EnergyCoupon with a conventional Time-of-Use (TOU) price-based DR program is also conducted. Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries, the effective cost for demand response providers in EnergyCoupon can be substantially reduced, while achieving a similar level of demand reduction as conventional DR programs.

Industrial Demand Response: How Network Tariffs and Regulation Do (Not) Impact Flexibility Provision in Electricity Markets and Reserves

Incentives for industrial loads to provide demand response on day-ahead and reserve markets are affected both by network tariffs, as well as regulations on the provision of flexibility in different markets. This paper uses a numerical model of the chlor-alkali process with a storable intermediate good to investigate how these factors affect the provision of demand response in these markets. We also model the effect of network tariffs and regulation on endogenous investment into process excess capacities, which are needed to provide load shifting. We find that fixed network tariffs based on peak-demand (demand charges) can be detrimental to the provision of demand response, especially to new investments in process capacity. For existing excess capacities, only high network tariffs inhibit demand response by limiting the optimal peak load below its physical limit. Marketing flexibility on the day-ahead market and in the reserves are substitutes for each other. The choice where to market flexibility is affected both by fixed peak-demand network tariffs and existing excess capacities. For endogenous investments, there are synergies between primary reserve participation and day-ahead flexibility provision, with the combination leading to increased capacity investments. In contrast, so-called interruptible load reserves, regular payments to industrial loads to be able to reduce electricity consumption at any point in time, incentivize a flat demand level. Consequently, such reserve markets reduce investments into additional flexibility capacities and often crowd out active participation in other markets.

Planning of an efficient power system with the use of modular static synchronous series compensation to enable flexible operational services

The need to move to a decarbonised electrical power system by 2050 has seen a dramatic rise in both renewable energy sources and demand response providers throughout transmission and distribution systems in Europe. This trend can not only be expected to continue but to rise rapidly into the future. It is expected that the systemic change in capabilities provided by this renewable energy to transmission and distribution system operation will be delivered, but the necessary associated network development to optimise their use has not. The lack of societal acceptance and the scale of the works have seen system development severely delayed. Consequently, better utilisation of existing assets will be required to continue to meet the increasingly challenging higher levels of decarbonisation measures in networks. Modular static synchronous series compensators (MSSSCs) offer a potential solution to this problem. From a simple lack of active and reactive power capacities to complex dynamic stability and power quality issues, MSSSCs act as a platform to provide a range of responsive measures. MSSSCs are versatile, compact, lightweight and redeployable. They offer a fast, flexible and cost-effective solution, which is justifiable to a society facing increasing uncertainty of future needs.

Short-Term Reliability Assessment of Generating Systems Considering Demand Response Reliability

Demand response (DR), one kind of flexible resources, can decrease the operating costs of power systems and improve their reliability. However, DR is not absolutely reliable due to its inherent uncertainty, so its function of improving system reliability is restricted. In order to estimate the risk of generating systems during the period of DR events, the short-term reliability assessment of generating systems considering DR reliability is studied in this paper. Firstly, a multi-state continuous-time Markov chain (CTMC) model of DR response capacity is established and the state division of response capacity is performed by the method of mean-standard deviation (MSD) classification. Secondly, the transition matrix of DR response capacity is estimated according to the sequence of DR response capacity. Thirdly, the CTMCs of demand response providers' (DRP) response capacity and those of generating units' (GU) output capacity are converted into universal generating functions (UGF) by Lz-transform. Finally, the transient distribution of DR response capacity and GU output capacity are derived, and the short-term reliability assessment method for generating systems considering DR reliability is proposed. A case study on a revised IEEE-RTS79 system shows the application of the presented method. The method proposed in this paper can assist system operators to evaluate the reliability of generating systems during the period of DR events.

Mechanism Design for Demand Response Programs

Demand response (DR) programs serve to reduce the consumption of electricity at times when the supply is scarce and expensive. Consumers or agents with flexible consumption profiles are recruited by an aggregator who manages the DR program. The aggregator calls on a subset of its pool of recruited agents to reduce their electricity use during DR events. Agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates of the energy an agent would have consumed if they were not participating in the DR program. Baselines are used to determine payments to agents. This creates an incentive for agents to inflate their baselines in order to increase the payments they receive. There are several newsworthy cases of agents gaming their baseline for economic benefit. We propose a novel self-reported baseline mechanism (SRBM) where each agent reports its baseline and marginal utility. These reports are strategic and need not be truthful. Based on the reported information, the aggregator selects or calls on agents with a certain probability to meet the load reduction target. Called agents are paid for observed reductions from their self-reported baselines. Agents who are not called face penalties for consumption shortfalls below their baselines. Under SRBM, we show that truthful reporting of baseline consumption and marginal utility is a dominant strategy. Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is assured to meet the load reduction target. Finally, we show that SRBM is almost optimal in the metric of average cost of DR provision faced by the aggregator.

Micro Water–Energy Nexus: Optimal Demand-Side Management and Quasi-Convex Hull Relaxation

This paper investigates the water network's potential ability to provide demand response services to the power grid under the framework of a distribution-level water-energy nexus (micro-WEN). In particular, the hidden controllability of water loads, such as irrigation systems, was closely studied to improve the flexibility of electrical grids. A optimization model is developed for the demand-side management (DSM) of micro-WEN, and the simulation results assert that grid flexibility indeed benefits from controllable water loads. Although the proposed optimal DSM model is an intractable mixed-integer nonlinear programming (MINLP) problem, quasi-convex hull techniques were developed to relax the MINLP into a mixed-integer convex programming (MICP) problem. The numerical study shows that the quasi-convex hull relaxation is tight and that the resulting MICP problem is computationally efficient.

Flexibility of Residential Loads for Demand Response Provisions in Smart Grid

One of the main advantages of smart grids is the presence of advanced communication technologies that facilitate grid operators and local distribution companies (LDCs) to communicate directly with customers. While at one end, the end-user customers seek to optimize their energy consumption by deploying home energy management systems (HEMSs), the LDCs on the other hand, seek to improve the grid operational efficiency and reduce losses. In this paper, a two-stage optimization framework is developed wherein multiple HEMS simultaneously optimize their respective energy consumption patterns, and determines their flexibility provision, which is communicated to the LDC. The LDC aggregates the controllable demand profile and the flexibilities of each HEMS to optimize its operational performance and hence the peak reduction signals which are sent to the HEMS. Studies are carried out considering a 33-bus distribution system coordinating with 1295 houses connected at different buses, and with varying customer preferences and objectives to demonstrate the applicability of the proposed scheme.

On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators.

Electrical load tracking scheduling of steel plants under time-of-use tariffs

Electrical load tracking is a critical strategy for energy intensive industries to provide demand response (DR) in today’s electricity markets. It can take advantage of time-of-use (TOU) tariffs in steelmaking-refining-continuous casting (SRCC) scheduling. In this paper, we develop a continuous-time mixed integer nonlinear programming (MINLP) model subject to energy-awareness and TOU tariffs to manage the electrical load tracking scheduling of SRCC. Due to the complex cases among load intervals of electrical load tracking, time-slots of TOU tariffs and processing cycles of jobs result from different time granularities of the electrical load tracking and TOU tariffs, we formulate the objective functions with derived general formulations that can apply to all cases. An improved strength Pareto evolutionary algorithm 2, AHSPEA2, is developed to solve this proposed model, whose search ability and population diversity are enhanced greatly by two strategies, the arithmetic crossover operator and the improved hybrid self-adaptive mutation operators. The computational results demonstrate that AHSPEA2 is far superior and prove its effectiveness in providing high-quality scheduling plans which follow the pre-contracted load curve carefully to decrease deviations and reduce electricity costs simultaneously.

A Novel Direct Load Control Testbed for Smart Appliances

The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.

Simultaneous power and heat scheduling of microgrids considering operational uncertainties: A new stochastic p-robust optimization approach

Uncertainties of renewable power generation, load, and electricity price imposes serious challenges in optimal scheduling of microgrids (MGs). To cope with this issue, advanced models should be developed to optimize scheduling problem of MGs under uncertainties. However, the optimality and robustness of solutions as well as the computational burden are still a matter of concern when applied to scheduling of a practical MG. To provide a model which is adjustably robust and computationally tractable and also financial effective at the same time, this paper proposes a novel uncertainty handling framework based on stochastic p-robust optimization method for day-ahead scheduling of MGs including renewable and non-renewable distributed generations, combined heat and power systems, energy-storage systems, as well as demand response providers. The objective is to find optimal scheduling of MG in a way that the expected profit is maximized while the regret in each scenario is bounded. The effectiveness of the proposed model is tested on a MG test system thorough different case studies. Simulation results demonstrate that the robustness of the scheduling results is enhanced significantly without substantial decrease in the expected profit of MG.