Demand Response Services Research Articles

Interoperability in demand response services for building systems interaction

Interoperability in demand response services for building systems interaction

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 decision-making model for joint energy and reserve scheduling of wind power producers with local intraday demand response exchange market

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

Stochastic programming based coordinated expansion planning of generation, transmission, demand side resources, and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

Data-driven model for real-time evaluating capacity of commercial buildings to participate in the demand response

The collective building stock is a major energy consumer, driving political discussions and forcing building owners to seek out cost-effective technologies. This creates a business opportunity in demand response (DR) services for commercial real estate. Despite recent efforts, developing a simple, scalable model remains an open question. We develop a real-time method for estimating electricity consumption profiles and inertia, enabling a bidding strategy on the DR market without compromising indoor climate. Accurate prediction of temperature changes is achieved through efficient models validated with real data.

Electric vehicle charging as a source of nordic fast frequency reserve—proof of concept

AbstractDemand‐side flexibility or demand response (DR) has long been recognized as a potential source of balancing capacity. The charging of electric vehicles (EVs) is a known source of flexible capacity, and the vast amount of charging capacity available can be utilized for valuable applications, including ancillary power markets, by controlling the charging sessions according to the needs of the power system. One identified and unexplored potential market for EV DR capacity is the Fast Frequency Reserve (FFR) market for the Nordic system area. This article proposes and demonstrates that a home charger can participate in this extremely fast reacting ancillary service market with only software modifications made to the charger. The biggest challenge in the FFR market is the response time requirement for capacity activation. When a certain frequency limit is exceeded, the activation must happen within 0.7 s in the worst case. In this study, a laboratory test setup was constructed to test the capabilities of the FFR‐enabled EV charger. The tests were conducted by building a laboratory microgrid capable of changing the grid frequency. Additionally, an economic feasibility study was carried out to evaluate the business potential of the concept in Finland. The economic study included an analysis of the concept's potential with different levels of DR service availability. The laboratory tests demonstrated the FFR capabilities of the charger and as a final outcome of the research, the Norwegian transmission system operator (TSO) approved the tested charger as a type‐prequalified FFR‐providing entity. The economic study revealed that the approach has good business potential, primarily because of the almost non‐existent cost side even if the availability rate decreases significantly.

Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy

Large-scale demand response (DR) is a promising solution to mitigate the problem of renewable energy (RE) curtailment with the rising proliferation of RE in both the transmission system (TS) and distribution system (DS). However, the current DR schemes face significant limitations when applied at a large scale, and the lack of consideration of TS and DS networks stymies the potential of large-scale DR in facilitating RE accommodation. To address these gaps, this paper proposes a novel hierarchical DR scheme based on locational customer directrix load (LCDL) that takes into account both TS and DS, involving interactions among tri-layer entities: the transmission system operator, distribution system operators, and customers. Firstly, the concept of LCDLs is proposed to characterize the desired load profiles at various locations, considering the constraints of both TS and DS networks. Subsequently, transmission-level and distribution-level LCDLs are formulated respectively and leveraged to induce the load reshaping of flexible resources at pertinent locations, thereby unlocking the deliverable flexibilities over TS and DS. Furthermore, the collaborative interaction among the three layers is depicted by a two-loop Stackelberg game, and a distributed algorithm is presented to achieve an equilibrium solution without compromising the privacy of the respective entities. Case studies testify that the proposed DR scheme significantly enhances the capacity to accommodate RE in both TS and DS, maintains economic balance in the trading process of DR services, and benefits all involved entities. Conducted on a practical city power grid with a substantial share of RE and DR customers, the simulation test validates the scalability of the proposed DR scheme and results show that with a 10% increment of DR customers in one DS, the RE curtailment rate could be reduced by 3% and 15% for TS and DS, respectively.

Game Theory Based Optimal Pricing Strategy for V2G Participating in Demand Response

Aggregators, as the interface of electric vehicles (EVs) and distribution system, are the market agents of EVs to participate in demand response (DR) and other balancing services. Aggregators can enlarge its benefit room from vehicle-to-grid (V2G) by providing flexible V2G price to EV users as long as they can secure a certain amount of V2G energy. There is a need to study Aggregator's V2G pricing strategy due to the fact that distribution companies, aggregators, and EV users have their own independent benefit maximization objectives as independent entities, and the decision variables of different entities interact and restrict each other. In this study, an optimal V2G pricing strategy for each aggregator is obtained by using Stackelberg game during the gaming of the involved multiple entities. The benefits of aggregator and EV users are set as the game factors, and benefit models of EV user's and aggregator's are established with consideration of the historical charging cost, and users' inconvenience cost. A simulation of V2G price for 750 EVs and 3 aggregators is carried out, and the impact of the market volume of EVs on each aggregator under this pricing strategy is studied. Simulation results show the feasibility of the proposed model and the interaction among the randomness of driving behavior and the market volume of EV groups, loading level of the distribution system, V2G strategies, as well as the DR capabilities.

Managing Considerable Distributed Resources for Demand Response: A Resource Selection Strategy Based on Contextual Bandit

The widespread adoption of distributed energy resources (DERs) leads to resource redundancy in grid operation and increases computation complexity, which underscores the need for effective resource management strategies. In this paper, we present a novel resource management approach that decouples the resource selection and power dispatch tasks. The resource selection task determines the subset of resources designated to participate in the demand response service, while the power dispatch task determines the power output of the selected candidates. A solution strategy based on contextual bandit with DQN structure is then proposed. Concretely, an agent determines the resource selection action, while the power dispatch task is solved in the environment. The negative value of the operational cost is used as feedback to the agent, which links the two tasks in a closed-loop manner. Moreover, to cope with the uncertainty in the power dispatch problem, distributionally robust optimization (DRO) is applied for the reserve settlement to satisfy the reliability requirement against this uncertainty. Numerical studies demonstrate that the DQN-based contextual bandit approach can achieve a profit enhancement ranging from 0.35% to 46.46% compared to the contextual bandit with policy gradient approach under different resource selection quantities.

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

This article quantifies and benchmarks the computational performance of the distributed alternating direction method of multipliers (ADMM) optimization algorithm applied to a slow-dynamics demand response peak shaving application. We propose a hierarchical demand response architecture, in which a commercial aggregator acts as the supplier and system-level flexibility service provider to a portfolio of residential prosumers. The prosumers are equipped with flexible distributed energy resources that we model based on convex operation constraints. The optimal day-ahead peak shaving control of the portfolio is realized in a distributed way through an entirely parallel implementation of the distributed ADMM optimization algorithm. In this scenario, we evaluate the ADMM algorithm's overall speed of convergence and conduct multiple large-scale numerical simulation experiments on a compute cluster to compare and assess the algorithmic performance of ADMM against a centralized reference benchmark optimization. Our simulation results show that the distributed ADMM algorithm is superior compared to a purely centralized optimization. The findings are of particular use to commercial demand response aggregators that show interest in the deployment and improvement of distributed coordination and optimal control concepts for future slow-dynamics demand response applications and services.

Multi-timescale interaction of a coupled electricity-hydrogen-gas system with a distribution network considering dynamic games

The large-scale access of new energy sources to distribution networks has led to its safe and stable operation. Therefore, this study considers a multi-timescale interaction of a coupled electricity-hydrogen-natural gas (EHG) system with a distribution grid for dynamic games. First, a multifaceted interaction mechanism between the coupling system and the distribution network balancing service mechanism, demand response service mechanism, and frequency regulation service mechanism was designed. Furthermore, an interaction framework between the coupled system and the distribution network was proposed, and a three-level time-scale interaction strategy between the distribution network and the coupled system was constructed considering the dynamic game in the medium- and long-term, short-term, and ultrashort terms. Subsequently, the presence and uniqueness of the interactive equilibrium solution of the dynamic game was proven. Finally, a region was used as an example for arithmetic analysis. The results of the calculation example indicate the following: 1) the ranges of interaction deviation under the medium-to long-term, short-term, and ultra-short-term time scales are [-3.76%, 2.84%], [-2.37%, 1.43%], and [-0.43%, 0.87%], respectively, indicating that the interaction deviation between the coupling system and the distribution grid can be reduced via adjusting and correcting the medium-to long-term, short-term, and ultra-short-term time scales. 2) Compared with the scenario in which only a single service is provided, the distribution network operation cost in the present study decreased by 15.92% and the revenue increased by 30.59%, signifying that the coupling system and distribution network diversification mechanism can expand revenue channels. 3) Compared with Scenario 1, without considering the cost of energy storage loss, and Scenario 3, with a single energy storage configuration, the actual service life of the battery in this study was the longest, 23.72% longer than Scenario 1, and 47.90% longer than Scenario 3, revealing that hybrid energy storage can achieve coordinated optimization and optimal allocation of resources at different time scales. 4) The interaction between the distribution network and coupling system, considering dynamic games, can form a fair price and guide to improved interaction between the distribution network and coupling system.

The economic value of hybrid battery swapping stations with second life of batteries

Battery Swapping Stations (BSS) can prove to be an integral part of the electric vehicle charging infrastructure and provide an alternative solution to the issue of range anxiety and long queuing times to charge the battery. Along with this, the increase in renewable integration in the grid as well as an increase in the number of discarded electric vehicle batteries offer another opportunity for infrastructure design while considering demand response services. In this paper, techno-economic feasibility of BSS considering the impact on electricity prices due to increased solar photovoltaics integration is analysed. An operational algorithm considering time of use tariff and estimated battery demand is designed to schedule battery charging in the battery swapping station with the objective of minimising the total charging cost. Four scenarios considering uncontrolled charging, smart charging, batteries discharging to the grid and second life batteries are designed and analysed. The results indicate that the charging cost reduces at least by 24% with smart charging and by 30% with battery to grid services. It is observed that the total charging cost can reduce further up to 19% while considering second life of batteries for storage. The study indicates the potential of novel business models in electrification of transportation, which can lead to increased opportunity for transition to sustainable transportation for developing nations.

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.

Impacts of Local Transit Systems on Vulnerable Populations in Michigan

Residents in small urban and rural areas frequently depend on unreliable personal transportation to maintain their lifestyle and get to essential destinations within their society. Especially, vulnerable populations, such as seniors and low-income residents, have lower access to personal vehicles and therefore are mobility-challenged. Being challenged in terms of mobility has effects on the health of the population concerned as this constraint not only limits economic activity that leads to perpetuation of lower means and poverty but also social activity that leads to physical and mental isolation. In a study of Michigan public and nonprofit transit systems, the customer satisfaction, particularly of vulnerable populations, with transit services is analyzed through on-board intercept surveys. Results illustrate the significance of employment, age, income, disability, and demand-response services in public transit planning and ultimately public health.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport

To provide an efficient demand-responsive transport (DRT) service, we established a model for predicting regional movement demand that reflects spatiotemporal characteristics. DRT facilitates the movement of restricted passengers. However, passengers with restrictions are highly dependent on transportation services, and there are large fluctuations in travel demand based on the region, time, and intermittent demand constraints. Without regional demand predictions, the gaps between the desired boarding times of passengers and the actual boarding times are significantly increased, resulting in inefficient transportation services with minimal movement and maximum costs. Therefore, it is necessary to establish a regional demand generation prediction model that reflects temporal features for efficient demand response service operations. In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. The proposed model considers a region’s unique characteristics and the influence between regions through spatial information, such as the proximity between regions, convenience of transportation, and functional similarity. In addition, three types of temporal characteristics—adjacent visual characteristics, periodic characteristics, and representative characteristics—were defined to reflect past demand patterns. With the proposed demand forecasting model, measures can be taken, such as having empty vehicles move to areas where demand is expected or encouraging adjustment of the vehicle’s rest time to avoid congestion. Thus, fast and efficient transportation satisfying the movement demand of passengers with restrictions can be achieved, resulting in sustainable transportation.

Customer perspectives on demand response in Europe: a systematic review and thematic synthesis

Residential demand response is increasingly recognized as a valuable tool to increase power-system flexibility and to improve integration of renewable energy sources in the sustainable energy transition. However, low customer participation and engagement is one of the barriers hindering widespread implementation of residential demand response. To improve understanding of the factors influencing customer engagement in residential demand-response programs, this article investigates associated customer experiences and attitudes. This study is based on a systematic review and thematic synthesis of findings from past demand response-pilot projects. By synthesizing findings from multiple sources, the article provides insights into the customer perspective to inform the development of customer-oriented demand-response services and products. The results indicate that customer engagement in demand-response programs is influenced by multiple factors, including motivation, interaction and communication, and feedback. Particularly highlighted is the value of social interaction and support as well as the importance of customer education and easily interpretable information. To promote customer engagement, the electric utility industry should place more focus on building customer relationships and integrating customer perspectives into the design of demand-response programs.

New Real-Time Demand Response Market Co-Optimized With Conventional Energy Market

In addition to procuring energy, consumers in electricity markets procure demand response (DR) services. Demand and supply of energy in the electricity market drives the demand for DR services. Through the net benefits test (NBT), economic procurement of DR is limited to an amount that ensures that consumers benefit with the procurement of DR services. However, the NBT neither a) recognizes the coexistence of the DR market with the energy market; nor b) optimizes social welfare in the DR market in concert with that of the energy market. This lack of accounting for DR market surplus results in economic inefficiency. To address this shortcoming, we advance past works by: a) proposing a real-time DR market where the DR demand curve is a function of opportunity in the energy market; and b) co-optimizing energy and DR markets such that the total social welfare derived from both markets is maximized simultaneously. We also present an optimal power flow formulation and process to implement our ideas in real-time electricity markets. The formulation is tested on a simple test case and a system based on actual Pennsylvania-New Jersey-Maryland (PJM) data. For the PJM case, total social welfare is increased by 1.41% to 3.05% over existing DR procurement strategies, resulting in $14.5M to $30.9M additional benefits per hour.

Demand-response oriented multi-dimension refined portrait of adjustable resources based on load and survey data fusion

Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers’ refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user’s nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user’s subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.