Demand Response Providers Research Articles

Model predictive control with self-learning capability for automated demand response in buildings

This paper presents an optimal management strategy, called Building Optimizer, based on a Model Predictive Control (MPC) approach with self-learning capabilities for buildings. The proposed MPC is a key enabler of cooperative demand response strategies at community level, ensuring the allocation of an optimal demand profile at each participating member of the community according to an optimal consumption reference defined by a complementary agent at community level. In this way, the proposed solution can contribute to provide demand response services exploiting the unlocked residential flexibility. The MPC calculates the optimal setpoints of the HVAC system’s terminal units, considering the expected usage of the buildings and the outdoor conditions, and exploiting the building’s thermal inertia. The models embedded in the MPC are grey-box models representing a thermal zone of the building. An adaptive MPC is proposed to mitigate the high uncertainty present in building management due to the time-variant characteristics of the building and the uncertain internal heat gains due to occupancy and other disturbances. For that, the reduced models incorporate self-learning capabilities implemented as Moving Horizon Estimators that perform a continuous calibration based on real-time measurements. This solution allows full automation for model calibration and management of the terminal units. This paper presents a case study to assess the thermal comfort assurance and power flexibility provision potential of the proposed solution. First, the Building Optimizer is compared to a regular benchmark consisting of an on–off controller, evaluating the performance of each controller for indoor temperature tracking. For the assessment of the flexibility provision, a baseline MPC with fixed model parameters obtained by an offline calibration is used for comparison to the Building Optimizer with self-learning capabilities. Different flexibility scenarios are simulated, considering different restrictions regarding thermal comfort and various flexibility request signals. Notably, the Building Optimizer outperforms the baseline MPC in all scenarios, particularly guaranteeing thermal comfort. Incorporating self-learning capabilities enhances controller performance by mitigating the effect of uncertainty, allowing the Building Optimizer to shift up to 13.08% of peak periods into valley periods, and tracking a flexibility request scenario of 10% with no discomfort.

A Review of Bidirectional Charging Grid Support Applications and Battery Degradation Considerations

Electric vehicles (EVs) are crucial in mitigating global emissions by replacing internal combustion engines. The capacity of EV batteries, coupled with their charging infrastructure, offers the added advantage of supplying flexible demand capacity and providing demand response benefits to the power grid, which is essential as overall demand increases. EVs ready for vehicle-to-everything (V2X) applications and chargers that support them enhance this flexibility by allowing for varied storage applications. However, to fully harness these benefits, it is vital to consider EV drivers’ charging habits and optimize the charging and discharging controls to minimize battery life impact. This study examines various V2X applications in North America and their effects on battery longevity, considering EV charging patterns. Additionally, it investigates advanced aging-aware optimization algorithms for managing bidirectional charging.

A linear reduced-order model for the activated sludge process for the integration into a mixed-integer linear energy system optimisation model

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

Distribution robust water-based demand side management in power transmission networks

The increasing share of renewable energy sources in the energy mix is posing new challenges for operating the power system. As a result, system operators must use the available flexibility more effectively to accommodate the surge in renewable energy. There are different ways to source flexibility including demand–response measures and sector integration. This paper investigates the utilization of flexibility from an integrated water and power network. A novel, augmented, distribution robust chance-constrained market clearing problem is proposed, assuming a centralized and fully competitive market set-up. Power generators and water pumps are modelled as flexible assets to procure flexibility from the integrated water and power network. Affine response policies and availability costs are defined to regulate the flexibility dispatch mechanism. The resulting coordination mechanism is nonlinear and non-convex. To make the problem feasible, different approximation and relaxation techniques are applied. A novel, hybrid, convex hull-based relaxation technique is proposed to convexify the friction head losses and pump energy consumption. The resulting problem is a second-order cone program which enables the power system operator to properly estimate and utilize the demand-side flexibility available in the water network. The results show that the electricity market benefits from grid flexibility obtained through the coordinated operation of controllable power generators and water pump loads. Under studied conditions, it was found that water pumps, when functioning as demand–response providers, can effectively compensate for power system imbalances, covering up to 25% of the total wind power mismatch. The sensitivity analysis emphasized the substantial impact of varying availability costs on the responses of flexible generators and water pumps. The error analysis identified the convex approximation of friction head losses as the primary source of inaccuracy in our model.

Residential flexibility characterization and trading using secure Multiparty Computation

The intermittent nature of renewable energy sources challenges the ability of grid operators to maintain balance between supply and demand of electricity. Demand response, or flexibility, is the ability of a load to modify its consumption in response to a control signal. The necessary two-way communications between end-users and grid operators introduce new risks on consumer privacy and data security. Consumer engagement in such programs tends to be low not only for privacy issues, but also because of the inappropriate incentive schemes. In this work, secure Multiparty Computation is used in a flexibility market where end-consumers provide demand response to grid operators in exchange for a monetary compensation. Multiparty computation is a cryptographic method that performs operations over encrypted data. Users submit their offers in encrypted form, and a novel way to characterize flexibility as a commodity and to determine device constraints with very little information from the consumers is developed. The market is cleared via a double auction-like process. The method is validated using flexibility offers from 200 devices of different types. We demonstrate the possibility to clear the market and allocate resources with encrypted orders for sole information.

An optimal multi-objective demand side management of a smart Microgrid consists of various building loads, considering relativity index and uncertainty

This paper addresses microgrid energy management in the presence of distributed generation and active loads while accounting for operational, economic, environmental, and reliability factors. The objective is to minimize the cost of shutdown during N-1 contingencies, projected emission-related expenses, and the overall operational cost of microgrids integrated with distributed generations. A stochastic scheduling technique that optimizes short-term microgrid operation, reducing costs and pollution through renewable resources is introduced. Utilizing demand response programs among residential, commercial, and industrial participants is proposed to counter the uncertainty of renewable resource-generated power. The demand response program implementation suggests considering financial incentives as bid prices, along with energy packages managed by demand response providers. Simulation results demonstrate three strategies to decrease operating costs and pollutants, contrasting scenarios with and without responsive load engagement. Equations governing AC power flow, microgrid operations, reliability, distributed generation, and, demand response programs and batteries, form the analytical core. Stochastic programming addresses uncertainties tied to demand, energy prices, renewable distributed generation production, and microgrid equipment availability. To attain dependable, optimal solutions with dynamic responsiveness, the combined ant lion optimization and crow search algorithm are employed. The proposed method is tested on a sample smart microgrid for validation. Numerical findings unequivocally underscore demand-side management potency in reducing power generation uncertainties from wind turbines and photovoltaics. This paper offers insights into microgrid energy management complexities, paving the way for resilient, cost-effective, and environmentally conscious energy distribution paradigms.

A Bi-Level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

Robust provision of demand response from thermostatically controllable loads using lagrangian relaxation

This paper presents an end-user privacy and thermal comfort preserving approach for residential thermostatically controllable loads (TCLs) to provide demand response (DR) in grid services under uncertainties. Unlike the standard approach whereby a central utility-level control is used to control end-users loads, this paper splits the control effort between a central coordinating controller and local robust controllers. The Lagrangian relaxation (LR) method is applied by relaxing the power tracking constraint to obtain a hierarchical control framework with a local controller at each household level and a coordinating controller at the central utility level. Household-level local controllers are based on robust model predictive control (MPC) that relies on minimum household-specific information while accounting for thermal model parameter uncertainties and forecast errors associated with exogenous inputs. Considering inverter-type air conditioners as the TCL, the approach is validated using a practical signal corresponding to the Australian Energy Market Operator. The results demonstrate that accurate tracking of the load set-point signal can be achieved under a considerable range of uncertainties while preserving thermal comfort for end-users. Furthermore, the proposed DR scheme is computationally tractable and robust for real-world implementation.

Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The increasing complexity of multi-energy coordinated microgrids presents a challenge for traditional demand response providers to adapt to end users’ multi-energy interactions. The primary aim of demand response providers is to maximize their total profits via designing a pricing strategy for end users. The main challenge lies in the fact that DRPs have no access to the end users’ private preferences. To address this challenge, we propose a deep reinforcement learning-based approach to devise a coordinated scheduling and pricing strategy without requiring any private information. First, we develop an integrated scheduling model that combines power and gas demand response by converting multiple energy sources with different types of residential end users. Then, we formulate the pricing strategy as a Markov Decision Process with an unknown transition. The novel soft actor-critic algorithm is utilized to efficiently train neural networks with the entropy function and to learn the pricing strategies to maximize demand response providers’ profits under various sources of uncertainties. Case studies are conducted to demonstrate the effectiveness of the proposed approach in both deterministic and stochastic environment settings. Our proposed approach is also shown to be effective in handling different levels of uncertainties and achieving the near-optimal pricing strategy.

Optimal allocation of demand response considering transmission system congestion

The increasing penetration of renewable energy sources in the electricity grid brings new operational challenges. This brings up the need for effective means to provide demand response in spite of its distributed nature throughout the grid. Aggregators can be created to manage a set of such demand response resources, but deciding how to allocate an aggregator’s resources is an important problem. One of the aspects that needs more attention is the impact of the transmission system on these decisions. In this paper, we propose a short-term optimization model for allocating demand response(DR) resources as well as generation resources to supply external demand that is offered after the scheduling decision is made. The DR resources will only be available for use after the scheduling decision is made. Finally, our work also considers the impact of congestion in the transmission system when allocating DR. We propose the use of a semidefinite relaxation to provide a good initial point to solve our model with the aim of guaranteeing that we will find an optimal solution. Results from numerical tests with the IEEE 96-RTS and the ACTIVSG500 test grids are reported. We found that DR resources mitigates congestion management, allowing the generators to supply more of the external demand that is offered. Besides that, we observe that using our proposed solution methodology, we were able to obtain optimal solution for both cases studies, which is not the case when solving the original formulation for the ACTIVSG500 grid.

An XGBoost-Based predictive control strategy for HVAC systems in providing day-ahead demand response

Heating, ventilation and air-conditioning systems (HVAC), at demand side, are regarded as promising candidates to provide demand response to smart power grids by adjusting indoor temperature setpoint. However, the optimal control of indoor temperature setpoint with the constraint of indoor environment is a key issue. In this study, an XGBoost-based predictive control strategy is proposed for HVAC systems in providing day-ahead demand response. XGBoost (Extreme Gradient Boosting) models are developed to predict the variation of power use and indoor temperature. Several innovations are implemented to improve the performance of the control strategy, including dedicated generation of the initial population of genetic algorithm (GA) in rolling optimization, the use of a step-decreasing control horizon, and the separation of the sampling period and control action period. The strategy is validated on a TRNSYS-Python co-simulation test platform. Results show that the developed XGBoost models can achieve a high prediction performance of power use (CV-RMSE of 4.52%) and indoor temperature (CV-RMSE of 0.40%) in the coming 10 min with only using one-week historical data. The computation cost is reduced by 78% due to the dedicated generation of GA initial population while the control performance is not affected. Compared with the traditional operation, the proposed control strategy can reduce the total cost by as much as 27.30% and 33.6% while the weighted indoor temperature only increases by 0.14 K and 0.62 K, respectively. In test cases, the highest Predicted Mean Vote (PMV) is 0.72 and the corresponding Predicted Percentage of Dissatisfied (PPD) is 15.9%.

Preemptive scheduling of EV charging for providing demand response services

We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution.

Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems

Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid’s energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers’ discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%,23.94%, 25.49%, and 31.65%) and (10.69%,3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system’s reliability with considering the weather uncertainty.

Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid

Integrating renewable energy resources (RES) is a challenge for power system operators due to their fluctuations and unpredictability. At the same time, the water shortage problem and the needs to desalinate more freshwater increase the prominence of sufficient energy resources for sustainable operation. Therefore, this paper presents a market-clearing mechanism in a co-optimization model that coordinates the operation of grid-connected reverse osmosis water desalination plants (RO-WDPs) and the operation of renewable-rich power systems. It is assumed that electric demands can participate in the provision of demand response (DR) to the market via multiple DR options. The DR options related to the general loads are demand shifting, load curtailment, distributed generation, and hybrid energy storage systems (HESS). These HESS includes battery storage and hydrogen storage systems. Pertaining to their special characteristics, a new DR option is proposed as a customized option for RO-WDPs. The market clearing mechanism is assumed to be based on the security-constrained unit commitment (SCUC) and formulated as a stochastic mixed-integer linear programming problem (MILP) to optimally schedule the day-ahead operation of the power system. The presented model is applied on 6-bus and IEEE 24-bus reliability test system (RTS) test power systems with significant penetration of RES to demonstrate its merits. The simulation results show that the system efficiency is enhanced by adding the energy flexibility of RO-WDP without endangering the water demand-supply when using the proposed coordinated model. Hence, the total operation cost is minimized, the RES integration is facilitated, and the hourly electricity prices are smoothened.

Dual-zone economic model predictive control of residential space heating for demand response using a single heat meter

Recent research has indicated a significant potential for providing demand response using Economic Model Predictive Control (E-MPC) of radiators in residential space heating systems. Previous studies indicate the need for distinct temperatures in different rooms, which would require an E-MPC with different temperature constraints across the dwelling. Studies on E-MPC concepts for this purpose often assume that it is possible to measure the heating power delivered to each room separately. However, this assumption is not valid in typical existing residential buildings heated by hydronic radiators; they only have one heat meter measuring the heating use of the entire housing unit. This study presents a simulation-based study of a novel dual-zone E-MPC scheme that relies on indoor air temperature measurements in individual rooms of the housing unit, and only one heat meter measuring the total heating use for space heating and domestic hot water. The results indicate that the proposed scheme has the same load shifting potential as an E-MPC using room-level heat metering. The proposed scheme thus reduces the additional hardware and data infrastructure needed for the practical realization of E-MPC of residential space heating systems for demand response purposes.

Optimal Scheduling of Battery-Swapping Station Loads for Capacity Enhancement of a Distribution System

A battery-swapping station (BSS) can serve as a flexible source in distribution systems, since electric vehicle (EV) batteries can be charged at different time periods prior to their swapping at a BSS. This paper presents an EV battery service transformation from charging to swapping batteries for EVs for the capacity enhancement of a distribution system. A novel mathematical model is proposed to optimally quantify and maximize the flexibility of BSS loads in providing demand response for the utility operator while considering technical operations in the distribution grid. Case studies and numerical findings that consider data from the National Household Travel Survey and a 32-bus distribution system are reported and discussed to demonstrate the effectiveness of the proposed model. Offering battery-swapping services helps reduce not only the peak load, but also the station operation cost.

Values of coordinated residential space heating in demand response provision

Demand side response from space heating in residential buildings can potentially provide huge amount of flexibility for the power system, particularly with deep electrification of the heat sector. In this context, this paper presents a novel distributed control strategy to coordinate space heating across numerous residential households with diversified thermal parameters. By employing an iterative algorithm under the game-theoretical framework, each household adjusts its own heating schedule through demand shift and thermal comfort compensation with the purpose of achieving individual cost savings, whereas the aggregate peak demand is effectively shaved on the system level. Additionally, an innovative thermal comfort model which considers both the temporal and spatial differences in customized thermal comfort requirement is proposed. Through a series of case studies, it is demonstrated that the proposed space heating coordination strategy can facilitate effective energy arbitrage for individual buildings, driving 13.96% reduction in system operational cost and 28.22% peak shaving. Moreover, the superiority of the proposed approach in thermal comfort maintenance is numerically analysed based on the proposed thermal comfort quantification model.

Two-stage stochastic programming for scheduling microgrids with high wind penetration including fast demand response providers and fast-start generators

A large portion of electricity demand in the world is delivered by microgrids (MGs). Operation of MGs under the uncertainty of demands and renewable power is a hot research area. As per the literature, in operation of MGs under uncertainty, the coordinated operation of MGs, with consideration of both day-ahead (DA) and real-time (RT) stages has not been investigated in details. In this paper, the operation of an MG with dispatchable generators and wind turbine has been formulated as a two-stage stochastic optimisation problem, wherein DA and RT stages are seen in one shot and DA and RT decision variables are determined in a way that the expected MG operation cost is minimised. In this research, the flexibility resources including fast responsive demands and fast-start distributed generators are used to increase the flexibility of MG and decrease its operation cost. The effect of the flexibility resources on MG operation has been investigated. Fast responsive demands accept scenario-dependent schedule, but slow responsive demands do not. Slow-start generators cannot be started up in any RT scenario, if they are OFF at DA stage, but fast-start generators can. • Two-stage stochastic optimisation has been used for scheduling microgrids. • Both day-ahead and real-time stages are seen in a single shot. • Fast responsive demands and fast-start generators are used as flexibility resources. • The effect of flexibility resources on MG operation has been investigated. • The uncertainties of demands and wind power have been considered.

Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications.

A Hierarchical Price-Based Demand Response Framework in Distribution Network

In the evolving local retail electricity market hierarchy, demand response providers (DRPs) are becoming viable interlink for the interaction between distribution system operator (DSO) and customers in demand response (DR) assessment in distribution network. This hierarchical structure is rendered in price based DR and is suggested as the proposed study. It is formulated as a tri-level two-stage DR in a theoretic game framework using two-loop Stackelberg game. In first stage, DSO (leader) and DRPs (followers) interact to determine optimal dynamic retail price, while optimizing their interests independently and in second stage, DRP as leader sets its optimal dynamic price to the customers (followers) for inducing DR. The existence and uniqueness of Stackelberg equilibrium is confirmed using backward induction and validated the optimality theoretically. It is formulated as nonlinear program with the consideration of ac network operating constraints. A nested reformulation and decomposition algorithm as a solution method is designed and is implemented to solve the problem. It is illustrated on IEEE 33-bus and a real 108-bus Indian distribution system. The detailed numerical results demonstrate the effectiveness of the proposed model and, scalability and tractability of the algorithm to solve the large scale problem reasonably.