Demand Response Providers Research Articles

Assessing the performance of global thermostat adjustment in commercial buildings for load shifting demand response

Abstract Efficiently leveraging new sources of flexibility is critical to mitigating load balancing challenges posed by variable renewable resources. The thermal inertia of commercial buildings allows us to shift their power consumption on minute to hourly timescales to provide demand response (DR) to the grid while maintaining occupant comfort. Global thermostat adjustment (GTA) provides a readily available and scalable approach for implementing load shifting DR using commercial heating, ventilation, and air conditioning (HVAC) systems, since it leverages the inherent sophistication of modern building automation systems. However, there is an incomplete understanding of GTA’s performance for this purpose and its impact on building systems and occupant comfort. In this paper, we explore the performance of GTA by analyzing results from nearly nine hundred experiments on eight university campus buildings in Michigan and North Carolina. Using GTA, we manipulate each building’s thermostat setpoints causing the building to shift its power consumption with respect to its baseline. We quantify the magnitude of HVAC power response, energy use of HVAC subsystems, and impact on occupant comfort. Finally, we connect our experimental results with power system operation using an optimization model that coordinates GTA actions across a large collection of grid-interactive efficient buildings to reduce high ramp rates on the grid and mitigate renewable energy curtailment. Overall, our work finds that the impacts on HVAC subsystems are often complex, and may result in additional energy being consumed by fans and terminal reheat. These effects must be considered when using GTA for load shifting. Additionally, we demonstrate that occupant comfort, as assessed by indoor temperature and humidity, can be maintained during GTA events. From a societal perspective, our modeling work finds that the additional renewable energy that can be integrated through the use of GTA strategies eclipses any additional energy consumed by buildings.

Building‐to‐grid optimal control of integrated MicroCSP and building HVAC system for optimal demand response services

AbstractThe world is shifting toward cleaner and more sustainable power generation to face the challenges of climate change. Renewable energy sources such as solar, wind, hydraulic are now the go‐to technologies for the new power generation system. However, these sources are highly intermittent and introduce uncertainty to the power grid which affects its frequency and voltage and could jeopardize its stable operations. The integration of micro‐scale concentrated solar power (MicroCSP) and thermal energy storage with the heating, ventilation, and air conditioning (HVAC) system gives the building greater leeway to control its loads which can allow it to support the power grid by providing demand response (DR) services. Indeed, the optimal control of the power flowing between the MicroCSP, the HVAC system, and the thermal zones can bring additional degrees of freedom to the building which can be relegated to the power grid based on the objective function and the incentives provided by the latter. This article presents an in‐depth investigation of the MicroCSP potential to provide ancillary services to the power grid. It focuses on evaluating the effect of incentives provided by the power grid on the building participation to the load following programs. It also demonstrates how the MicroCSP can help the building deal with constraints related to load peak shaving and ramp‐rate reduction set by the power grid as part of long‐term DR contracts. A sensitivity analysis is carried out to confront the results to prediction uncertainties of the energy prices and the weather conditions.

Preemptive Scheduling of Ev Charging for Providing Demand Response Services

Preemptive Scheduling of Ev Charging for Providing Demand Response Services

Multi-Agent Consensus Control Scheme for the Load Control Problem

With the help of smart grid technologies, a lot of electrical loads can provide demand response to support the active power balance of the grid. Compared with centralized control methods, decentralized methods reduce the computational burden of the control center and enhance the reliability of the communication. In this paper, a novel second-order multi-agent consensus control method is proposed for load control problem. By introducing the velocity state into the model, the proposed method achieves better performance than traditional ones. Simulation results verify the effectiveness of the proposed method.

Uncertainty modelling of an industry facility as a multi-energy demand response provider

While the latest European energy regulations emphasise the active power system participation of the household level end-users, the large industrial facilities are still not fully exploiting all the market opportunities to decrease their costs and become more competitive. Significant cost reduction can be achieved by offering flexibility services in the electricity market. This is especially valid in the case when the industrial consumers are multi-energy hubs where shifting and optimising usage of input energy vectors creates additional opportunities. Research gaps were identified and a price responsive demand response model for a multi-energy industry facility under uncertainty was developed. The uncertainty aspects are modelled both by the robust optimisation and by the two-stage stochastic optimisation. Additionally, we develop a linear energy flow-based model of an industrial steam system which better encompasses losses and makes the model more realistic. The model is validated on a real-world case of a multi-energy industry facility and the results indicate that cost savings of up to 18 % can be achieved compared to the passive and deterministic, mass flow-based business-as-usual behaviour.

Provision of demand response by French prosumers with photovoltaic-battery systems in multiple markets

Provision of demand response by French prosumers with photovoltaic-battery systems in multiple markets

Dynamic modeling of solar-assisted ground source heat pump using Modelica

Traditional rural energy sources, such as straw, firewood, and coal are the main energy resources in developing countries; however, these resources are associated with low efficiency and can cause considerable air pollution. With the rapid development of renewable energy worldwide, existing energy frameworks in rural areas need to be urgently transformed. Developing a universal and reliable dynamic modeling framework for multi-source heat pumps remains an unresolved issue. To this end, this paper proposes an integrated energy system based on a solar-assisted ground source heat pump (SGSHP), which can facilitate the utilization of renewable energy, reduce the electricity consumption, and provide demand response (DR) resources in rural houses. A 150-m2 rural house on the outskirts of Shanghai, China was considered as a case building. A novel Dymola modeling framework of an SGSHP system was established, and the heating performance was analyzed. Considering the variation in the energy demand in winter, four practical operation modes were implemented to maximize energy efficiency. The results show that the coefficients of performance of the system are 4.2 and 3.5 with and without solar collectors, respectively. The contributions of solar energy, geothermal heat, and electricity in the system are 27%, 18%, and 55%, respectively. Moreover, approximately 79.6% of AC loads can be shaved during evening peak load time in our proposed energy system.

Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model

In this paper, a hybrid power generation company consisting of a concentrated solar power unit, wind turbines, a battery system, and a demand response provider is established to take part in electricity markets. The operating strategy of the hybrid power generation company in day-ahead and adjustment (intraday) markets is determined based on their coordinated operation. To tackle the intrinsic uncertainties, for the first time, a mixed stochastic-interval model is proposed which addresses the uncertainty in demand response and solar energy via interval optimization. The examined problem is formulated as a multi-objective optimization problem in which the risk of both stochastic and interval parameters can be involved. On this basis, the proposed operating strategy covers three objective functions, namely, expected radius and midpoint of the hybrid power generation company's profit together with the conditional value-at-risk. Accordingly, the normal boundary intersection and lexicographic optimization techniques are utilized to derive feasible solutions. Lastly, numerical results are presented and the performance of the proposed framework is investigated. The results indicate that the suggested model can be efficiently used to handle the decision-maker's preference over interval and stochastic parameters, and the risk criterion associated with interval parameters becomes larger as the forecasting errors increase.

Blockchain Technology Applied to Energy Demand Response Service Tracking and Data Sharing

Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.

Transactive Energy Market Framework for Decentralized Coordination of Demand Side Management Within a Cluster of Buildings

Flexibility in electricity demand can be leveraged for demand side management (DSM) to enable aspects such as “demand following generation” and provide ancillary services to support grid integration of renewable and distributed energy resources. The upcoming connected devices in demand centers such as commercial buildings and households may be leveraged for this. However, the coordination among distributed demand centers in a scalable decentralized manner to achieve DSM objectives is still a challenge. In this article, a generalized hierarchical transactive energy based multiagent framework is proposed. This framework includes energy management demand agents (EMDAs) at the building level, which coordinate the operation of different appliances within the buildings. EMDAs also actively participate on day-ahead Walrasian market at the cluster of buildings level. In this market, the effect of wide-adoption of DSM on generation dispatch is also studied. A particular instance of this framework is also implemented in a cyber-test system consisting of standard industrial microcontroller platforms to emulate practical implementations via smart meters. Experimental results of the proposed system are included to demonstrate the possibility of avoiding disptach from expensive generation units by reduction in peak demand and providing demand response inherently.

Correction to “Integrating EV Charging Stations as Smart Loads for Demand Response Provisions in Distribution Systems” [Mar 18 1096-1106

In [1] , an additional affiliation for Omar Hafez was not included. The affiliation information in the first footnote should read as follows:

Modeling of unforced demand response programs

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

Increasing the Number of Installed Residential Heat Pump Water Heaters in the USA Through Improved Technology and Utility Programs

Purpose of ReviewHeat pump water heaters (HPWHs) offer an efficient alternative to electric resistance water heaters, but they suffer from limited market penetration. This paper reviews the current state of HPWHs in the USA, focusing on technology developments and programs to increase adoption. What technological improvements are needed to make HPWHs a good fit in more homes? What are the best ways of promoting HPWHs to consumers?Recent FindingsImproved communication and controls will allow HPWHs to provide demand response and follow dynamic utility rates. To enable fuel-switching, HPWHs are being developed that only need a standard 120V outlet. Split HPWHs can alleviate the cooling and noise that limit the use of HPWHs in small homes, multifamily buildings, and cold climates. Additionally, new utility programs aim to train installers and provide incentives to distributors to increase the number of HPWHs installed.SummarySome utilities offer novel incentive programs that have led to increased customer uptake. Those advances, coupled with improved controls for load shifting and new products such as split HPWHs and HPWHs that can run on 120V power, may finally move the market forward.

Evaluation of advanced control strategies of electric thermal storage systems in residential building stock

This paper investigates the effect of different control strategies applied to electric thermal storage systems to provide demand response services. These results indicate how policymakers or manufacturers could target the implementation of advanced control on electric thermal storage systems and apply these to households characterised by different occupancy profiles, thereby making demand response initiatives more attractive to end users.

Privacy-Preserving Transactive Energy Management for IoT-Aided Smart Homes via Blockchain

With the booming of smart grid, The ubiquitously deployed smart meters constitutes an energy internet of things. This paper develops a novel blockchain-based transactive energy management system for IoT-aided smart homes. We consider a holistic set of options for smart homes to participate in transactive energy. Smart homes can interact with the grid to perform vertical transactions, e.g., feeding in extra solar energy to the grid and providing demand response service to alleviate the grid load. Smart homes can also interact with peer users to perform horizontal transactions, e.g., peer-to-peer energy trading. However, conventional transactive energy management method suffers from the drawbacks of low efficiency, privacy leakage, and single-point failure. To address these challenges, we develop a privacy-preserving distributed algorithm that enables users to optimally manage their energy usages in parallel via the smart contract on the blockchain. Further, we design an efficient blockchain system tailored for IoT devices and develop the smart contract to support the holistic transactive energy management system. Finally, we evaluate the feasibility and performance of the blockchain-based transactive energy management system through extensive simulations and experiments. The results show that the blockchain-based transactive energy management system is feasible on practical IoT devices and reduces the overall cost by 25%.

A Peer-to-Peer Energy Trading Framework for Wind Power Producers With Load Serving Entities in Retailing Layer

This article proposes a peer-to-peer (P2P) energy trading framework for wind power producer (WPP) in the retailing layer to increase its revenue and to promote wind power utilization. In this framework, the WPP can provide energy consumption of demand response providers (DRPs) in a competitive environment through both the main grid and rival load-serving entities. Also, based on a P2P pricing mechanism, the WPP can choose if to trade energy in a P2P platform or not. The proposed problem is formulated as a stochastic bilevel optimization model, in which in the upper level, the WPP profit is maximized and in the lower level, the DRPs participate in DR programs and tend to choose the fairest supplier among the WPP and LSEs to minimize their energy procurement costs. The proposed method is applied to a realistic case study and the results demonstrate that with P2P, the WPP can schedule the energy transaction with peers to offset part of the energy deviations. Moreover, different values of P2P prices lead to different values of energy transactions due to the relatively diversity pattern of local generation of rival LSEs and their offering prices and demand and generation of the WPP.

Coordinated Multi-Time Scale Optimal Control of Building Energy Systems for Demand Response Considering Forecast Uncertainties

As an economical way to address the great challenge from increasing renewable energy generation of intermittent nature, demand response is attracting increasing attention recently. Predictive scheduling is widely used to optimize energy dispatch of building energy systems for demand response, since it can minimize cost considering time-varying electricity price. However, it probably cannot satisfy energy demands and achieve optimized demand response in actual operation due to forecast uncertainties. In this study, a coordinated multi-time scale optimal control strategy is therefore proposed to optimize energy dispatch between building energy systems for demand response considering forecast uncertainties. The control strategy coordinates a stochastic scheduling scheme and a real-time optimal control scheme using coordinating control variables. The selection of coordinating control variables is comprehensively analyzed. The optimal start time of scheduling optimization horizon is identified. Forecast uncertainties are quantified based on real meteorological data. The proposed control strategy was tested and evaluated on the energy system of the Zero Carbon Building in Hong Kong. Results show that it is essential and beneficial to consider forecast uncertainties and coordinate stochastic scheduling with real-time optimal control to ensure satisfaction of energy demands and minimization of energy cost in actual operation when providing demand response.

Risk-constrained self-scheduling of a hybrid power plant considering interval-based intraday demand response exchange market prices

Hybrid power plants (HPPs) integrating dispatchable and non-dispatchable generation are gaining attention by generation companies due to their increased flexibility in the operation of the power system. In this paper, an offering and bidding framework for an HPP consisting wind, photovoltaic (PV), compressed air energy storage (CAES), battery energy storage (BES), and thermal units in day-ahead (DA) and intraday markets is presented. Moreover, the interaction between the HPP and demand response providers (DRPs) through the intraday demand response exchange (IDREX) market is incorporated into the proposed model. The existing uncertainties such as DA, intraday, imbalance prices, along with renewable energy and IDREX market, are tackled via a hybrid stochastic-interval approach. The suggested structure is not only capable of handling both stochastic and interval uncertainties but also can manage the risk associated with both uncertainty characterization methods. To this end, the proposed risk-constrained offering and bidding model turns into a tri-objective optimization problem in which the normal boundary intersection (NBI) procedure is applied for its solution. The numerical results demonstrate that the proposed framework is well capable of simultaneously reaching risk-taker and risk-averse strategies.

Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage

Heating, ventilation, and air conditioning (HVAC) systems, combined with the internal thermal mass of buildings, have been deemed to be promising means of providing demand response (DR) resources, particularly for buildings with active energy storage systems. DR resources, such as peak-load reduction potential, can provide grid-responsive support resulting in a high degree of grid involvement and high flexible electricity demand. In the DR field, the potential of HVAC load flexibility has been considered in buildings. In the future smart buildings, it is important to take advantage of demand-side resources to achieve real-time energy supply–demand balance sustainably. In this context, DR potential and characteristics of buildings play a pivotal role in DR programs. However, few studies have investigated the internal thermal mass’s heat release and DR characteristics of buildings. Thus, a systematic experiment is conducted to study the DR potential and characteristics of internal thermal mass and active storage systems. The DR resources include the passive cooling storage from furniture, building envelope and an active water storage tank. Two DR control strategies, including pre-cooling and temperature resetting, are analyzed in this study. The experimental results show that the strategies are effective for short-term (0.5 h) and intermediate-term (2 h) DR programs. For a long-term DR program, active energy storage technology such as a water storage tank is required to satisfy the occupant's comfort requirements. Hence, we conclude that passive thermal mass and active storage systems should be simultaneously considered in practical DR programs for better DR implementation.

Optimal Behavior of a Hybrid Power Producer in Day-Ahead and Intraday Markets: A Bi-Objective CVaR-Based Approach

Coordinated operation of various energy sources has drawn the attention of many power producers worldwide. In this paper, a Concentrating Solar Power Plant (CSPP) along with a wind power station, a Compressed Air Energy Storage (CAES) unit, and a Demand Response Provider (DRP) constitute the considered Hybrid Power Producer (HPP). In this regard, this paper deals with the optimal participation of the mentioned HPP in the Day-Ahead (DA), and intraday electricity markets by benefiting from the joint configuration of all accessible resources. To attain risk-averse strategies in the suggested model, Conditional Value-at-Risk (CVaR) based on the ε-constraint technique is employed, while its efficiency is validated compared to the previously applied method to such problems. On the whole, the main contributions of this work lie in: 1) proposing a novel model for optimal behavior of a CSPP-based HPP in DA, and intraday markets using a three-stage decision-making architecture, and 2) developing a bi-objective optimization framework to improve the functioning of the risk-constrained algorithm. Simulation results reveal that taking advantage of the CSPP in the intraday market, and coordinated operation of all resources not only enhance the profitability of the system but also lessen the associated risk compared to the previous models.