Demand Response Signals Research Articles

Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps

The flexibility of electrical heating devices can help address the issues arising from the growing presence of unpredictable renewable energy sources in the energy system. In particular, heat pumps offer an effective solution by employing smart control methods that adjust the heat pump’s power output in reaction to demand response signals. This paper combines imitation learning based on an artificial neural network with an intelligent control approach for heat pumps. We train the model using the output data of an optimization problem to determine the optimal operation schedule of a heat pump. The objective is to minimize the electricity cost with a time-variable electricity tariff while keeping the building temperature within acceptable boundaries. We evaluate our developed novel method, PSC-ANN, on various multi-family buildings with differing insulation levels that utilize an underfloor heating system as thermal storage. The results show that PSC-ANN outperforms a positively evaluated intelligent control approach from the literature and a conventional control approach. Further, our experiments reveal that a trained imitation learning model for a specific building is also applicable to other similar buildings without the need to train it again with new data. Our developed approach also reduces the execution time compared to optimally solving the corresponding optimization problem. PSC-ANN can be integrated into multiple buildings, enabling them to better utilize renewable energy sources by adjusting their electricity consumption in response to volatile external signals.

Future-proof rates for controlled electric vehicle charging: Comparing multi-year impacts of different emission factor signals

Electricity pricing can be used to shift the timing of electricity demand, but the choice of price signals is highly constrained. Consumer rates are updated every few years and limited to simple daily profiles, yet must capture the complex dynamics of a changing electricity system. Emission factors (EFs) were developed as an evaluation tool, but are increasingly used as demand response (DR) signals. Given these constraints, can they be effective? We evaluate the emissions impact of EF-based electricity rates with and without supply-side emissions pricing. We study controlled electric vehicle (EV) charging in the Western U.S. up to 2037 by coupling an electricity system dispatch model and a data-driven EV charging model. We compare average and short-run marginal EFs with a new medium-run marginal EF that better matches the timeline of electricity rate updates. We find that a stable supply-side signal makes DR more valuable: DR reduces emissions by up to 6% with supply-side carbon pricing or just 2% without it. Medium-run marginal EFs yield the most consistent emission reductions, but constraints on charging flexibility limit their impact. We recommend policymakers base rates for DR on medium-run marginal emission factors and implement supply-side carbon pricing to facilitate greater emission reductions.

Enhancing sustainable urban economies: A statistical investigation of transitioning from fossil fuel-dependent industries to green innovation hubs

As a result of the evolution of the smart grid, the new energy market has been restructured in urban systems, whereby domestic users play a more active role in the demand response (DR) programs with a view to balancing production and demand. DR incentive signals, though, are difficult to respond to due to limited understanding by users. As a result, an Energy Management Controller (EMC) has been expanded for responding to DR signals and solving energy management problems. The EMC employs a whale optimization algorithm (WOA) for automatically responding to the DR signal and solving energy management issues by planning 3 kinds of domestic load: non-interruptible, interruptible, and hybrid. With the WOA, the EMC generates the best home load plan for reducing power costs, carbon emissions, PARs, and customer dissatisfaction. Simulations were accomplished in order to evaluate the offered WOA against current algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and teaching learn optimization algorithm (TLBO). The outcomes indicated that the suggested WOA reduces electricity costs, carbon emissions, PARs, and consumer dissatisfaction better than other algorithms.

The positive and negative impact of novel utility demand response programme and stochastic utility-driven events on renewable penetration and flexibility activation of train and station systems

Integrating an incentive-driven demand response program (DRP) into a metro railway system can enhance the techno-economic benefits of grid operators and metro railway operators by modulating the grid interactions. To facilitate demand response (DR), all energy flexibility resources within the metro network, such as heating, cooling, electrical, and vehicle systems, should be individually regulated by optimal control strategies. Therefore, an automated energy management system was developed and implemented to prioritise and logically control various energy flexibility resources to maximise the annual performance based on the requirements of the DR signal from the utility grid. Automated DR management helps to achieve an annual DR incentive of 0.57 × 106 HKD, enabling a 2.9 times lower annual operational cost of 0.28 × 106 HKD compared to the reference, even after accounting for the annual electricity tariff of 0.84 × 106 HKD. The techno-economic impacts of becoming a DR participant were stochastically studied using Hong Kong’s DRP by examining all crucial factors in the DR business model. In addition, this study defines a methodology for determining the optimal demand reduction target without biasing any stakeholders. Then, the DR business model was comparatively evaluated between a prosumer-based energy system and the same system without renewable energy support. The former system loses the DR incentive in proportion to the load matching compared to the latter system. Moreover, the DRP was extended from static to dynamic DR support, and the results show that considering dynamic DR can further increase the economic benefit of all stakeholders.

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

Unlocking ventilation flexibility of large airport terminals through an optimal CO2-based demand-controlled ventilation strategy

Large airport terminals consume significant energy due to their extensive ventilation demands. Optimizing the operation of ventilation systems is a crucial aspect of achieving greater energy flexibility and efficiency. In this study, we proposed an optimal CO2-based demand-controlled ventilation (DCV) strategy that utilizes the large indoor space of airport terminals to shift ventilation loads, reduce operating cost, and enable demand response (DR) programs. The effect of air infiltration on ventilation demand is considered in the DCV strategy through on-site CO2 measurement. A passenger dwell model is integrated to estimate the occupancy of airport terminal based on flight schedules for dynamic ventilation control. Considering the DR signal and time-of-use tariff, an advanced evolutionary algorithm is employed to locate optimal ventilation solutions that deliver better economic performance and energy flexibility while meeting indoor air quality constraints. Using an airport terminal in Beijing, China, as a testbed, the superior performance of the proposed DCV strategy was demonstrated against a baseline strategy with a fixed outdoor air supply rate under three scenarios. Simulation results showed that the proposed DCV strategy can reduce peak energy consumption by up to 60.9% and operating costs by up to 48.6% in different scenarios. Moreover, the DCV strategy can provide a flexible trade-off between the overall energy efficiency and DR capability in the adjustable DR scenario. This study revealed great opportunities for improving the flexibility of ventilation in large-space buildings and provided an instructive case study.

Demand-Side Regulation Provision of Virtual Power Plants Consisting of Interconnected Microgrids Through Double-Stage Double-Layer Optimization

This study proposes a double-stage double-layer optimization model for a virtual power plant (VPP) consisting of interconnected microgrids (IMGs) with integrated renewable energy sources (RESs) and energy storage systems (ESSs) to realize demand-side ancillary service, considering intra energy sharing among the IMGs within the VPP. In particular, the first stage, day-ahead scheduling, is carried out to predict the hourly electricity consumption baseline and regulation capacity for the next day, the latter of which results in a reward from the market operator. In the second stage, real-time power consumption control is performed by following the dynamic regulation (or RegD) signal. The second stage is further divided into two layers: the upper layer distributes demand response (DR) signals from the main grid according to the electricity unit price of each microgrid (MG) and exchanges electricity among MGs based on a new energy sharing mechanism to reduce RegD-following violations. The lower layer performs real-time power consumption control for each MG to minimize operation costs. The overall goal is to maximize the reward in the day-ahead stage and minimize the RegD-following violation penalty in the real-time stage, so as to minimize the overall operation cost of the VPP. The optimization is written in five objective functions, which are solved using mixed integer linear programming (MILP) in Gurobisolvers. Extensive simulation and comparison studies are carried out, and numerical results show that compared with traditional MG operations, VPPs comprised of IMGs can reduce operation costs and provide better frequency support for the grid through superior RegD signal following performances.

Optimization of Distributed Energy Resources in Distribution Networks: Applications of Convex Optimal Power Flow Formulations in Distribution Networks

The increasing penetration of distributed energy resources (DER) has imposed several challenges in the analysis and operation distribution networks. In the last decade, the implementation of battery energy storage systems (BESS) in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. However, a complex formulation of the optimization problem regarding DER implementations can easly become nonconvex, thus limiting the scope of the results (quality) and affecting the computational efficiency. In this paper, convex formulations of the optimal power flow (OPF) problem for DER (PV and BESS) installations in San Andres distribution network were implemented to minimize power losses. Four main simulation cases were stablished covering convex power flow analysis, convex optimal power flow to locate and size dispatchable and nondispatchable DER (PV) with demand and irradiation profiles, and the inclusion of aggregated and distributed PV and BESS systems. The computational realization of those formulations was made with three different software packages: CVX in python, CPLEX, and MATLAB (MATPOWER/PSO). The results show that installing DER capacity (both PV and/or BESS) can substantially improve the power losses in distribution networks, especially if considered distributed instead of aggregated, achieving reductions up to 75% for power losses and up to 57% for conventional generation utilization. Besides power losses, it was observed that optimal DER (PV and BESS) implementations behaved similarly as demand response signals do, potentially increasing economic benefits for DISCOs. In terms of computational efficiency, convex formulations help substantially to reduce computation times, especially if scalability is to be considered when distributed DER installations were studied.

Quantum computing for future real-time building HVAC controls

Buildings contribute to more than 70% of overall U.S. electricity usage and greenhouse gas (GHG) emissions. HVAC systems in buildings often consume more than 40% of the total building energy usage. To reduce such high energy use, numerous control strategies including optimal and predictive controls have been developed and demonstrated. To achieve a near real-time solution, most previous research has simplified the non-linearity of building thermodynamics and provided an approximate optimal solution. The future HVAC control optimizes more connected devices in buildings, which requires a rapid and accurate response, not only to the building itself but also to the grid signals. It also poses the challenge of solving non-linear problems with discrete variables. With the recent development of quantum computers, this has become feasible. In this paper, we developed a new optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU). Compared to traditional optimization methods, we obtained similar solutions with less than 2% differences and improved computational speed from hours to seconds. We also demonstrated an 80% reduction in total electricity consumption and a 21% reduction in electricity bills by considering day-ahead price time-of-use demand response signals. Quantum computing has proven capable of solving large-scale non-linear discrete optimization problems for building energy systems.

Economic Scheduling of Gaseous-liquid Hydrogen Generation and Storage Plants Considering Complementarity of Multiple Products

The accessible and convenient hydrogen supply is the foundation of successful materialization for hydrogen-powered vehicles (HVs). This paper proposes a novel optimal scheduling model for gaseous-liquid hydrogen generation and storage plants powered by renewable energy to enhance the economic feasibility of investment. The gaseous-liquid hydrogen generation and storage plant can be regarded as an energy hub to supply concurrent service to both the transportation sector and ancillary market. In the proposed model, the power to multi-state hydrogen (P2MH) process is analyzed in detail to model the branched hydrogen flow constraints and the corresponding energy conversion relationship during hydrogen generation, processing, and storage. To model the coupling and interaction of diverse modules in the system, the multi-energy coupling matrix is developed, which can exhibit the mapping of power from the input to the output. Based on this, a multi-product optimal scheduling (MPOS) algorithm considering complementarity of different hydrogen products is further formulated to optimize dispatch factors of the energy hub system to maximize the profit within limited resources. The demand response signals are incorporated in the algorithm to further enhance the operation revenue and the scenario-based method is deployed to consider the uncertainty. The proposed methodology has been fully tested and the results demonstrate that the proposed MPOS can lead to a higher rate of return for the gaseous-liquid hydrogen generation and storage plant.

ANFIS-BCMO technique for energy management and consumption of energy forecasting in smart grid with internet of things

This paper proposes an efficient energy management approach for managing the demand response and energy forecasting in a smart grid using Internet of Things (IoT). The proposed energy management approach is the hybrid technique that is the joint execution of adaptive neuro fuzzy inference system (ANFIS) and balancing composite motion optimization (BCMO), thus it is called ANFIS-BCMO technique. An energy management approach is developed using price-based demand response (DR) program for IoT-enabled residential buildings. Then, we devised a approach depends on ANFIS-BCMO technique to systematically manage the energy use of smart devices in IoT-enabled residential buildings by programming to relieve peak-to-average ratio (PAR), diminish electricity cost, and increase user comfort (UC). This maximizes effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings on smart cities. The ANFIS-BCMO technique automatically responds to price-based DR programs to combat the main problem of DR programs that is the limitation of the consumer’s knowledge to respond when receiving DR signals. For consumers, the proposed ANFIS-BCMO based strategy programs appliances to exploit benefit based on reduced electricity bill. By then, the proposed method increases the stability of the electrical system by smoothing the demand curve. At last, the proposed model is executed on MATLAB/Simulink platform and the proposed method is compared with existing systems.

A home energy management system incorporating data-driven uncertainty-aware user preference

Today, with the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. Traditionally, complicated thermal models or passive participation of the users prevents HEMS from fully automating the involvement of demand-side energy management. In this paper, an advanced HEMS is proposed incorporating uncertainty-aware user preference. The energy consumption user behavior, including temporal and temperature habits, is firstly characterized in a data-driven way with non-intrusive load monitoring (NILM). To capture the potential uncertainties resulting from the characteristics of NILM modeling, a novel NILM model is developed with Bayesian theory. The NILM-based preference level is further integrated into the HEMS to schedule the appliances and respond the demand response (DR) signals for economic benefits. Extensive experiments are performed with the real-world dataset. The effectiveness and superiority of the proposed algorithm are demonstrated particularly in reducing the energy cost, maintaining the user’s preference level, and encouraging users to participate in DR. Compared to a traditional HEMS as a benchmark, the proposed HEMS for a 24-hour horizon can trade-off limited electricity costs to keep the preference at a high level.

Price-based low-carbon demand response considering the conduction of carbon emission costs in smart grids

The rapid development of the Industrial Internet-of-Things extends demand response (DR) research to the aspect of low-carbon emission in smart grids. This study proposed the concept of low-carbon DR (LCDR) in the electricity market as well as the price-based LCDR mechanism and its model. First, carbon cost conduction from the generation side to the demand side was analyzed, and then conduction function was quantifiably deduced. Second, the mechanism and model of price-based LCDR were proposed by considering three DR signals, namely, the electricity price, carbon price, and carbon emission intensity of the demand side, based on the traditional price-based DR (PBDR) mechanism. Third, the proposed LCDR mechanism was applied to the environmental–economic dispatch optimization problem. At last, case studies on the modified IEEE 39-bus system verified that the LCDR mechanism can reduce carbon emissions while maintaining the function of the traditional PBDR. Meanwhile, the applicability of LCDR was illustrated based on carbon emission sensitivity to LCDR model parameters. The proposed mechanism can guide participants in the electricity market in reducing electricity carbon emissions.

Electric Vehicles Charging Algorithm with Peak Power Minimization, EVs Charging Power Minimization, Ability to Respond to DR Signals and V2G Functionality

The number of electric vehicles (EV) on the roads, as well as the share of EVs in use, will inevitably increase in coming decades. This creates a number of problems. A large EV fleet is a significant additional load in the power system that is impossible to accurately predict. Another related problem is the limited distribution network capacity, which is not ready for the additional load from the widespread EV infrastructure. There is a need for an EV charging coordination algorithm capable of fulfilling the charging EV needs, while using as low demanded power as possible and using the lowest power values in each EV charging profile. We propose an EV coordinating algorithm that is capable of ensuring that all connected EVs in the considered parking lot will be charged at the user-defined departure time. The algorithm also controls the charging/discharging power of every connected EV in such a way that the parking lot as a whole will use minimal possible peak power while minimizing the charging power of every EV. The proposed algorithm is also capable of responding to demand response (DR) signals. The paper also includes the results of simulation with a statistical summary of the proposed algorithm effectiveness.

Proactive Rolling-Horizon-Based Scheduling of Hydrogen Systems for Resilient Power Grids

Deploying distributed energy resources and other smart grid technologies have increased the complexity of power grids and made them more vulnerable to natural disasters and cyber-physical-human (CPH) threats. To deal with these extreme events, proactive plans are required by utilities to minimize the damages caused by CPH threats. This article proposes a proactive rolling-horizon-based scheme for resilience-oriented operation of hydrogen (H2) systems in integrated distribution and transmission networks. The proposed framework is a bilevel model in which the upper level is focused on distribution system operation in both normal and emergency operation modes, and the lower level problem accounts for the transmission network operation. Two preeminent aspects of H2 systems are considered in this article. First, to show the flexibility of H2 systems, capacity-based demand response signals are considered for electrolyzers, stationary fuel cell (FC) units, and H2 storage tanks are considered in both normal and emergency operation modes. Second, unlike the batteries that can only charge and discharge energy based on maximum duration times and power ratings, H2 systems can be considered as the flexible long-term energy storage by storing H2 for days and supplying power to FC in the case of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-m</i> outages lasting for more than 10 h. Moreover, the H2 production cost based on water electrolysis and storage costs is calculated. Simulation results demonstrate that utilities can improve the system-level resilience using H2 systems as long-term backup power resources.

Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach

The implantation of demand response programs may be unsuccessful due to a variety of reasons. One of the most important is the so-called response fatigue, which refers to the discouragement experienced by consumers when they received an excessive number of signals from the operator. This circumstance is, however, typically ignored in energy management tools of electrical energy systems. To solve this issue, this paper proposes an uncertainty-aware day-ahead optimal scheduling tool for grid-connected microgrids based on information gap decision theory, which incorporates additional constraints to bound the duration of demand response signals. Thereby, the harmful effects caused by response fatigue are lessened. The developed optimization problem is formulated as a Mixed-Integer-Linear programming, which is solvable using standard solvers and versatile enough to be adapted to different system layouts. A benchmark case study serves to show the effectiveness of the developed methodology to manage with uncertainties, while the effect of response fatigue in consumers is bounded to acceptable thresholds. As a sake of example, the developed methodology is able to determine a scheduling plan with a total renewable generation 50% lower compared with the deterministic case, while the total demand is overestimated by ∼20%, in which the effect of response fatigue is kept within acceptable bounds yet. Accurateness and efficiency of the new proposal are also checked by making a comparison with other uncertainties modelling.

Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid

With the smart grid development, the modern electricity market is reformatted, where residential consumers can actively participate in the demand response (DR) program to balance demand with generation. However, lack of user knowledge is a challenging issue in responding to DR incentive signals. Thus, an Energy Management Controller (EMC) emerged that automatically respond to DR signal and solve energy management problem. On this note, in this work, a hybrid algorithm of Enhanced Differential Evolution (EDE) and Genetic Algorithm (GA) is developed, namely EDGE. The EMC is programmed based with EDGE algorithm to automatically respond to DR signals to solve energy management problems via scheduling three types of household load: interruptible, non-interruptible, and hybrid. The EDGE algorithm has critical features of both algorithms (GA and EDE), enabling the EMC to generate an optimal schedule of household load to reduce energy expense, carbon emission, Peak to Average Ratio (PAR), and user discomfort. To validate the proposed EDGE algorithm, simulations are conducted compared to the existing algorithms like Binary Particle Swarm Optimization (BPSO), GA, Wind Driven Optimization (WDO), and EDE. Results illustrate that the proposed EDGE algorithm outperforms benchmark algorithms in energy expense minimization, carbon emission minimization, PAR alleviation, and user discomfort maximization.

Impact of demand response on occupants\u2019 thermal comfort in a leisure center

Many assets that are normally installed during an energy-efficient building retrofit can also be used to provide flexible services to the electricity grid. By turning off or turning down some mechanical systems during peak times, it is possible for a building to reduce its load on the electricity network. A field demand response event was simulated at a leisure center in Ireland to evaluate the suitability of the site to participate in the Irish demand response market, to assess how much flexibility it can provide, how much the indoor conditions changed during the test, and to examine whether these remained within satisfactory limits. A survey was conducted to determine whether the occupants perceived any changes to their thermal comfort. The simulation was achieved by identifying non-critical mechanical equipment and turning them off for 2 h. A processing station for demand response and energy monitoring delivered the demand response signal to the site’s building management system. The results show that this site had a flexibility potential of 45 kW, which is considered too low to participate in the demand response market, as Irish aggregators favor sites that can offer over 250-kW flexibility. However, the indoor thermal conditions remained within reasonable ranges and the occupants did not notice the impact of the demand response event. This shows that theoretically, if smaller sites were allowed to sell their flexibility to the electricity market, such leisure centers could participate in demand response services without impacting occupants’ comfort.

Functional model of residential consumption elasticity under dynamic tariffs

One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific, which poses additional barriers for the active engagement of consumers in these programs. The elasticity of consumers’ demand behavior varies from individual to individual. The utility will benefit from knowing more accurately how changes in its prices will modify the consumption pattern of its clients. This work proposes a functional model for the consumption elasticity of the DR contracted consumers. The model aims to determine the load adjustment the DR consumers can provide to the retailers or utilities for different price levels. The proposed model uses a Bayesian probabilistic approach to identify the actual load adjustment an individual contracted client can provide for different price levels it can experience. The developed framework provides the retailers or utilities with a tool to obtain crucial information on how an individual consumer will respond to different price levels. This approach is able to quantify the likelihood with which the consumer reacts to a DR signal and identify the actual load adjustment an individual contracted DR client provides for different price levels they can experience. This information can be used to maximize the control and reliability of the services the retailer or utility can offer to the System Operators.

Estimating the participation value of electricity demand-response programmes for a two-stage production system

ABSTRACT Electricity demand-response programmes, such as the incentive-based and price-based programmes, have been used by utilities to induce customers to reduce their electricity consumption during peak periods. This study investigates the production decisions of a two-stage production system under these programmes in a situation where peak periods arrive randomly in the manufacturing cycle. Analytical results show that under demand-response programmes, the manufacturer, who aims at minimising the total operational cost, usually selects a lower production rate during peak periods and a higher one during non-peak periods. Notably, the uncertainty of the peak periods also has a significant influence on the manufacturer's production plans under these programmes. This paper further investigates the efficiency of different demand-response programmes in reducing the inventory holding cost and electricity cost. The results indicate that participating in the demand-response programmes does not always result in a higher inventory holding cost, which goes against the manufacturer's intuition about these programmes. In addition, this paper evaluates the manufacturers' preference for these demand-response programmes by comparing the operational cost savings generated from participating in the two programmes. It turns out that both the demand-response signals and the inventory holding cost substantially influence the manufacturer's willingness to participate in the programmes.