Demand Response Programs Research Articles

Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level

Energy storage such as battery and thermal energy storage is an effective approach to shift building peak load and alleviate grid stress at a building cluster level. However, due to the heterogeneous performance of different types of storage (e.g., response speed, charge/discharge efficiency and rate, storage capacity) and highly diversified energy use patterns of individual buildings, the multi-energy storage should be properly selected and optimally designed for individual buildings to achieve effective load shifting. The optimal deployment of multi-energy storage at a cluster level is a challenging optimization problem due to the nonlinear dynamic performance of the multi-energy storage and the high dimensionality as a result of a large number of buildings. To tackle the challenges, this study proposes a data-driven surrogate optimization method that optimally deploys multi-energy storage at a cluster level to minimize the building cluster energy bill under demand response programs. The method utilizes data-driven surrogate models to accurately predict demand response performance of individual buildings with multi-energy storage. An iterative optimization with automated energy-storage-option screening is developed to optimize the multi-energy storage configurations and design parameters. For a case study including 21 buildings, by optimally deploying multi-energy storage including battery, cooling TES tank, and building-integrated TES, the method reduced the building cluster energy bill by 8%–181% as compared to baseline cases. The optimal deployment method effectively identifies the buildings with better potential to adopt demand-side management and balances the pros and cons of the energy storage options, increasing demand response incentives by 12%–31%. The proposed method can be used in practice to facilitate the deployment of energy storage and improve engagement of buildings in demand response.

Combined peak reduction and self-consumption using proximal policy optimisation

Residential demand response programs aim to activate demand flexibility at the household level. In recent years, reinforcement learning (RL) has gained significant attention for these type of applications. A major challenge of RL algorithms is data efficiency. New RL algorithms, such as proximal policy optimisation (PPO), have tried to increase data efficiency. Additionally, combining RL with transfer learning has been proposed in an effort to mitigate this challenge. In this work, we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline. We evaluate our approach on a demand response use case where peak shaving and self-consumption is incentivised by means of a capacity tariff. We show our adapted version of PPO, combined with transfer learning, reduces cost by 14.51% compared to a regular hysteresis controller and by 6.68% compared to traditional PPO.

Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning

Greenhouses are key to ensuring food security and realizing a sustainable future for agriculture. However, to ensure crop growth efficiency, greenhouses consume a significant amount of energy, primarily through climate control and artificial lighting systems. Owing to this high energy consumption, a network of greenhouses exhibits immense potential to participate in demand response programs for power grid stability. In this work, a multi-agent deep reinforcement learning (MADRL) control framework utilizing an actor-critic algorithm with a shared attention mechanism is proposed for energy management in networked greenhouses. A network of renewable energy integrated greenhouses is constructed to interact with the power grid, when necessary, to address the fluctuations associated with renewable energy generation and dynamic electricity prices. The viability and scalability of this multi-agent approach is demonstrated by evaluating its capabilities for a network of five greenhouses of varying capacities. The proposed MADRL-based control approach for demand-side energy management in networked greenhouses demonstrates efficiency in maintaining indoor climate in all greenhouses while ensuring a 28% reduction in net load demand as compared to well-known algorithms.

Barriers to demand response in the commercial and industrial sectors – An empirical investigation

Demand response can be an effective mechanism to balance electricity demand and supply. While there is an increasing research interest in demand response for private households, its realistic potential in industrial and commercial sectors remains largely untapped, with limited research on the barriers hindering adoption. This study identifies and examines the barriers in industrial and commercial sectors, shedding light on their significance. We conducted 20 semi-structured interviews with experts from German industrial and commercial companies and national industry associations. The most frequently cited barriers encompass concerns about diminished product quality, disruptions to production processes, human resource management, and revenue uncertainty. Despite some recent industrial participation in demand response programs in Germany, our findings suggest that numerous barriers impede widespread participation, which can partly be explained by the heterogeneity of production processes and facilities. Overcoming these barriers entails bridging knowledge gaps and allocating sufficient resources within an organization. Moreover, adapting external incentives and policies may be necessary to encourage widespread demand response adoption. Recognizing these challenges, alongside the underlying motivations and apprehensions, can guide policy makers in devising strategies to support the adoption of demand response among industrial and commercial consumers.

Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles

This research introduces a stochastic scheduling approach that incorporates risk constraints for an energy hub (EH), considering uncertainties related to renewable generation and load demands. The proposed method utilizes the Conditional Value at Risk (CVaR) technique to assess and quantify risks. By striking a balance between reducing operational and emissions costs and increasing risk aversion, the approach presents a trade-off. The EH comprises various components such as a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP), and plug-in electric vehicles (PEVs). Uncertain variables encompass factors such as wind speed, solar irradiation, different demands, and market prices. To optimize profits and enhance the consumption curve, demand response programs (DRPs) for electrical, thermal, and cooling demands are implemented. To address the uncertainties associated with input random variables, the efficient k-means data clustering method is employed. A new slime mold algorithm, based on coughing and chaos theory, has been proposed to enhance the problem's solution. The algorithm incorporates innovative operators to improve its capabilities. By utilizing the coughing mechanism and chaos theory, the algorithm explores the solution space more effectively, resulting in improved outcomes for the problem at hand. The results demonstrate significant flexibility in EH management and are extensively discussed. Simulation results indicate that integrating PEVs, FCPP, and DRPs can lead to reductions of 2 %, 7 %, and 11 % in the EH's operating costs, respectively. Furthermore, considering PEVs, FCPP, and DRPs can improve the EH's risk cost by 1.98 %, 6.7 %, and 10.5 %, respectively. Based on the numerical results, in Case 4 led to a remarkable 12.65 % reduction in operational costs while simultaneously achieving a 15.43 % decrease in emission costs, showcasing the effectiveness of the proposed approach in optimizing energy management in an energy hub system.

Demand response programs: Comparing price signals and direct load control

Canada's electric power system is responsible for approximately 9 % of national emissions, 53 % of which occur in the province of Alberta. Integrating new variable renewable energy resources is a key part of the supply-side decarbonization pathway, while end-use electrification unlocks further opportunities on the demand side. The inherent variability of variable renewable energy output necessitates network flexibility. Supply-side flexibility solutions require significant investment, but demand-side management programs have potential to deliver network flexibility at a lower cost. An integrated framework consisting of demand and supply models investigates the efficacy of demand response programs for improving network flexibility in Alberta's power system, as measured by multiple operational metrics. The framework is applied to two demand response programs: real-time pricing and direct load control. These programs are assessed for two generation capacity mixes derived from an expansion planning model. Results indicate that substantial network flexibility benefits are achievable in a zero-emission power system via both programs resulting in avoided wind curtailment by 7.7 %. Improvement in operational costs, up to 1.4 % of the base output, is also observed in all scenarios accompanied by savings in household power bill expenditure by up to 15 %. These improvements, however, are only achieved fully when sufficient flexible load is provided.

Economic, Technical, and Environmental Benefits of Occupants’ Thermal Comfort-Based Heat Loads Participation in Demand Response Program

The electrification of heating and transportation systems is one of the objectives of developed countries to minimize CO2 emissions. This objective pushes distribution systems’ (DBs) operators to incorporate numerous high-power loads into low-voltage networks that were not designed for such loads. DBs’ reconfiguration and the loads’ flexible characteristics are two remedies exploited to overcome this issue. Heat pumps (HPs), as the most prevalent loads, can be managed by demand response programs (DRPs) to postpone the costly reconfiguration of distribution systems. HPs’ DRP participation affects indoor air temperature. If this is not accomplished reasonably, the occupants’ thermal comfort (OTC) will be compromised, and it will be impossible to convince them to continue contributing to DRPs. Based on the ASHRAE55 standard and an experimental building electrothermal model, this article presents a novel framework for determining the HPs’ DRPs participation. This framework ensures the OTC and optimizes the HPs’ DRP participation. The modified IEEE 33-bus network is employed as the test system to evaluate the proposed method. The simulation results confirm the usefulness of the proposed strategy to improve the technical and economic aspects of the network.

Leveraging flexibility of residential heat pumps through local energy markets

The integration of variable renewable energy sources such as wind and solar energy has made demand-side flexibility a critical aspect for balancing the power grid during fluctuating power generation. In recent years, heat pumps have gained increasing attention for their flexibility potential. While demand response programs have been extensively discussed to leverage flexibility, the market-based approach of local energy markets (LEMs) requires more attention. LEMs provide a marketplace for local energy exchange, facilitating a more balanced energy system by harnessing the flexibility of heat pumps at lower costs. Therefore, this study examines the economic benefits of leveraging flexibility of heat pumps through LEMs and the problems that may arise. An agent-based simulation of LEMs with double-sided auctions is utilized to consider prosumer behavior, incorporate model-predictive control, and employ detailed modeling of energy devices. According to the findings, a district where 40% of households use heat pumps can reduce their annual cost by 5.1% through a LEM. The study identifies several factors contributing to the relatively small economic benefits, including high balancing costs, excessive taxes and network charges, uneven distribution of benefits, and seasonal fluctuations. Additionally, the study proposes daily demand charges to mitigate high residual demand peaks resulting from heat pumps. In conclusion, the study emphasizes the key role of heat pumps in achieving economic benefits through LEMs and highlights the regulatory framework changes required to effectively tackle the challenges faced by LEMs with a large share of heat pumps.

Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail.

Two-Stage experimental intelligent dynamic energy management of microgrid in smart cities based on demand response programs and energy storage system participation

Power variations due to uncertainties create fluctuations in voltage/frequency (V/F). Most critical microgrid’s (MG) challenge in smart cities is V/F stability considering uncertainties in different operating conditions. This study proposes an energy management platform based on an intelligent probabilistic wavelet petri neuro-fuzzy inference algorithm (IPWPNFIA) to control the V/F index in the presence of renewable energy sources (RESs) and battery energy storage system (BESS) facing with various uncertainties. The suggested approach is programmed at two central and local controller stages based on the communication system and time-of-use demand response programs execution. The suggested approach is modeled by considering asymmetric membership functions based on the BESS optimal participation to control uncertainties caused by RESs, plug-and-play operations, and load fluctuations. The proposed platform's performance is verified and compared in different scenarios with different methods. The experimental setup and results are based on the rapid control prototyping of the micro-grid platform, MATLAB/Simulink and RT-LAB software, and hardware infrastructure such as the OPAL-RT (OP5600/OP8660) System. The most important highlights of this research are: fast dynamic response, real-time control based on real data, reducing the calculation time and burden based on learning algorithms, and the suitable coordination to adjust the protection equipment pick-up time.

A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data

Demand-side management (DSM) is crucial to smart energy systems. This paper presents a data-driven approach for understanding the relationship between energy consumption patterns and household characteristics to better provide DSM services. The proposed method uses a robust learning fuzzy c-Means clustering algorithm to automatically generate the optimal number of customer groups for DSM, and then uses symmetric uncertainty techniques to identify the identified load patterns and socio-demographic characteristics as the features for training a deep learning model. The model obtained can be used to predict the possibility of DSM group membership for a given household. This approach can be applied even in situations where smart meter data are not available, such as when new customers are added to the system or when residents change, or due to privacy concerns. The proposed model is evaluated comprehensively, including prediction accuracy, comparison with other baselines, and case studies for DSM. The results demonstrate the usefulness of weekly energy consumption data and associated household socio-demographic information for distinguishing between different consumer groups, the effectiveness of the proposed model, and the potential for targeted DSM strategies such as time-of-use pricing, energy efficiency measures, and demand response programs.

Shape-based clustering for demand response potential evaluation: A perspective of comprehensive evaluation metrics

With the wide deployment of smart meter and advanced communication technology, customers in demand side have large potential to participate in demand response (DR). Therefore, suitable DR potential evaluation methods are needed to select customers for proper DR programs. In this work, an evaluation procedure based on multiple metrics is proposed. First, a bi-level clustering method is proposed to group load profiles into clusters based on the similarity in shape. Adaptive k-Shape clustering is proposed to automatically generate clusters based on the defined threshold. To evaluate the variability of the load profiles in each cluster, adaptive piecewise aggregate approximation (APAA) method is proposed to divide time series into segments based on load features. Then, one-step Markov chain is utilized to model the dynamics of load profiles. And the multiple evaluation metrics is calculated which can reflect variability, sensitivity, and quantity of electricity consumption to generate potential evaluation criterion. A numerical case study based on Low Carbon London dataset is conducted. The results show that the proposed method is feasible in terms of cluster division, metric calculation, and customer selection.

Opening the black box of demand response: Exploring the cognitive processes

Evaluations of price-based demand response programs tend to focus on users' electricity use patterns and/or their practical experiences. Less is known about the effects that price-based demand response programs have on cognitive drivers and barriers to energy-using behaviors and habits, or how well these predict timing of households' electricity use. This study seeks to address this gap by evaluating the effects of a mandatory demand-based time-of-use distribution tariff, using electricity-meter and questionnaire data in an intervention and a reference area, and a structural equation model following the theory of planned behavior. Although no effect was found of the tariff on the actual proportion of peak-hour use, there were significant effects on users’ intentions and motivations to shift electricity use to off-peak hours. The absence of effect on the proportion of peak-hour use seems explained by the facts that only a minority of consumers were aware of their tariffs, and by the (at least partially correct) beliefs that consumers used very little electricity and most of it was already used in off-peak hours. The relationships between intentions, drivers and the actual proportion of peak-hour use were stronger in the intervention area, compared to the reference area. Interestingly, this was true not only for the motivation targeted by the tariff, economic savings, but also for sustainability concerns and social norms. This suggests that effects of the tariff may partly run via other non-monetary motivators.

Assessing the viability and environmental impact of residential demand response programs: A case study in East Legon, Greater Accra, Ghana

Residential demand response programs hold significant potential for delivering cost benefits and enhancing the overall security of the power supply network. In the context of Ghana, the current state of such programs could be improved. Regrettably, utility companies in the country currently lack an understanding of customers' behavior related to electricity use, which hinders residential demand response implementation in Ghana. This paper aims to relieve the Ghanaian power network’s demand in peak hours through a residential demand response program by assessing consumer behavior related to electricity consumption in a day. This study presents East Legon as a case and employs the diversified demand method based on consumer behavior to assess residential demand response in Ghana. The results indicate that a 3 MW of demand can be relieved on the network in a day during peak hours. The annual energy savings resulting from this initiative is 40,479,991 kWh. A 1,032,239,780 kg of CO2 emissions due to this program can be avoided. Notably, the study demonstrates a positive Net Present Value (NPV) of GHS 350.00, signifying the financial viability of the demand response program in East Legon over the specified time horizon. The research underscores the significance of leveraging consumer behavior to optimize demand response programs, offering valuable guidance to policymakers and utility companies to enhance energy efficiency and sustainability in the region.

Eco‐environmental operation of energy hub considering uncertainties of supply/demand‐side resources with introducing a novel optimization algorithm

AbstractIn this paper, the optimal operation of Energy hub (EH) based upon both demand and renewable energy resources (RESs) uncertainties is investigated. Here, an innovative scenario‐based energy hub energy management system (EH‐EMS) is proposed for simultaneous cost and emission reduction considering the abovementioned uncertainties. Moreover, a multi objective hybrid heuristic algorithm, the so‐called hBES‐MOGWO, is introduced for solving the proposed EH‐EMS, which is a combination of bald eagle search (BES) and multi objective gray wolf optimization (MOGWO) algorithms. The proposed EH consists of photovoltaic (PV), wind turbine (WT), boiler, heat storage (HS),combined heat and power (CHP) and multi‐carrier energy storage technology such as power to gas (PtG) technology, electric vehicles and heat storages. Flexible loads are also taken into account for reducing cost and emission by participation in demand response programs (DRPs). The optimization results demonstrate the effectiveness of the proposed hBES‐MOGWO algorithm. The simulation results illustrated that the total cost of EH‐EMS can be effectively reduced by installing energy storage systems and implementing DRPs. The results emphasize that considering uncertainties can lead to increase the operating costs and emission, while flexible loads participation in DRPs and PtG can reduce the aforementioned ones.

Flexibility enhancement of multi-district DISCOs considering a trade-off between congestion and extractable reserve capacity from virtual energy storage systems

One of the fundamental concerns of distribution companies (DISCOs) is how to satisfy security requirements under uncertain conditions and real-time contingencies. To address this concern, it is vital to employ operational flexibility levers aimed at mitigating congestion and retaining optimal reserve capacity, particularly in critical feeders. Increased interaction between DISCOs and virtual energy storage systems (VESSs) offers an unparalleled opportunity to unlock the energy flexibility of distribution grids at the local level without increasing operational complexity. According to the state-of-the-art, models that tap the economic performance of VESSs while respecting the flexibility and security setpoints adopted by DISCOs in a distributed manner have remained scarce. This study goes beyond by developing a flexibility-oriented decision-making strategy as a single-leader multi-followers stochastic model; in which a multi-district DISCO involves the flexibility setpoints and congestion management strategy in the market participation plan of each VESS locally to enhance the energy flexibility of each district. In the upper level, DISCO aims to deal with stressful situations in real-time sessions by (i) determining the minimum reserve capacity requirements in each district to be provided by VESSs with respect to the existing uncertainty sources and the importance of each district, and (ii) alleviating the congestion in critical feeders to release the feeder capacity for deployment of the available reserve power promptly. In the lower level, each VESS makes optimal decisions on power and reserve bidding in day-ahead, reserve, and real-time markets that are affected by the flexibility services required by the DISCO. Inspired by the activity schedule of different users, it also examines how demand response programs can affect DISCO’s ability to unlock consumer-side flexibility in realizing congestion management strategies. The developed bi-level scheduling model is first reformulated as single-level mathematical program with equilibrium constraints (MPEC) by employing Karush–Kuhn–Tucker (KKT) optimality conditions and then linearized into a mixed-integer linear program (MILP). The proposed strategy is validated through extensive simulations conducted on the modified IEEE 33-bus test system consisting of three districts with different users. Furthermore, DIgSILENT PowerFactory is used to verify the feasibility of the proposed model under contingency cases and to provide insights for DISCOs to control multiple VESSs and capacity-correlated uncertainties.

Application of Internet of Things in Residential Distribution Systems

Enabling an internet of things (IoT) application in residential distribution systems by integrating houses with IoT windows and occupant behavior can provide numerous advantages to the power grid, including, but not limited to, demand diminution, congestion reduction, and capacity deferral. This paper presents a new framework that mathematically enables an IoT application in residential distribution systems by integrating IoT windows and occupant behavior with houses for load management and energy conservation. With the proposed framework, we model residential loads considering the IoT concept, and then develop a mathematical optimization model that facilitates the integration of IoT-based houses into the residential distribution system. Different case studies considering a 33-bus distribution network are presented and discussed to demonstrate the effectiveness of penetrating IoT-based houses on distribution system operations and household profitability. It is observed that the profit of the local distribution company decreases when houses are transformed to IoT-based houses due to the fact that less energy is sold to the households. On the other hand, the operation cost of the IoT-based house is lower than that of the conventional house because of the better-managed house energy use, thereby resulting in saving money. It is found that 10% and 20% penetrations of IoT-based houses help reduce the maximum power imported through the distribution substation by 30 kW and 60 kW, respectively. It is also found that the load of IoT-based houses and power availability of a rooftop photovoltaic generation are not compatible, and hence, without an action from the customer and/or utility to coordinate them through a demand response program, IoT-based houses would not contribute to increasing the connectivity of PV-distributed generation in the smart grid.

Integrated planning framework for microgrid incorporating the flexibility provision capabilities of demand response programs

The penetration of renewable energy sources (RESs) in power systems has led to an increase in the system’s flexibility needs. An integrated planning framework for long-term capacity sizing and short-term operation planning of an isolated microgrid (MG) using the flexibility capabilities provided by optimal demand response (DR) strategies is presented in different scenarios. DR programs incentive optimization is applied for omitting or reducing the mismatch between consumption and generation profiles to achieve economic investment and operation planning. The evaluated scenarios include the combination of photovoltaic (PV) system, wind system, battery, supercapacitor, and pumped heat energy storage (PHES). The objective function is to minimize the annual costs of MG design and operation. A new method is proposed for hourly pricing of DR programs based on the amount of charging and discharging of the energy storage system for isolated MGs. The combination of PV, wind, and PHES systems is found to be the most cost-effectiveness configuration for the MG. The proposed DR program reduces the total costs of MG by 15.89% and increases system flexibility.

The computation of the air conditioning loads life loss and its application to demand response

AbstractAir conditioning loads (ACLs) represent an increasing proportion of power system loads, offering significant potential for optimised scheduling and active participation in demand response (DR) programs. While many studies have focused on ON/OFF control schemes that satisfy system requirements, few have addressed quantifying the life loss of ACLs from the user perspective. To address this gap, a quantitative model of ACL life loss is established and an optimal scheduling model is developed for ACLs participating in DR that incorporates the cost of life loss. The relationship between life loss and refrigeration power is a complex non‐linear high‐order fractional function that cannot be solved by commercial solvers. Therefore, a bi‐objective multi‐weight optimisation algorithm is proposed with a complex non‐linear fraction based on the Dinkelbach algorithm and its feasibility through mathematical examples is verified. Finally, a numerical example based on the IEEE 39‐bus test system is provided to demonstrate the feasibility of the model and the effectiveness of the proposed solution method.

What influences industrial enterprises’ willingness of demand response: A survey in Qinghai, China

Although demand response (DR) is an important means for solving the stability and reliability challenges of renewable energy (RE) power systems, it remains unclear how DR can be widely applied in industrial enterprises, particularly in areas with a high proportional RE system and industrial energy consumption that do not apply any DR programs. The objective of this study is to understand the influencing factors of industrial enterprises' willingness to participate (WTP) in DR in these areas, including one economic factor and three noneconomic factors. This study performs a questionnaire survey in Qinghai, and a total of 556 industrial enterprises completed valid questionnaires, these data are empirically analyzed through Structural Equation Model. The results demonstrate enterprises' DR awareness degree is the most significant predictors of the WTP in DR. Meanwhile, enterprises' electricity consumption level also had a significant impact on the WTP in DR rather than enterprises' potential DR capabilities and the expected minimum compensation. Finally, the study suggests feasible recommendations, such as increasing enterprises' ability of analyzing potential DR, directly encouraging enterprises to participate in DR, designing compensation standards and adopting differentiated DR according to enterprises’ actual DR potential.