Demand Response Programs Research Articles

Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Stochastic energy planning of a deltoid structure of interconnected multilateral grids by considering hydrogen station and demand response programs

This paper presents triple-objective stochastic energy planning and management of a deltoid structure in which a microgrid, nano-grid, and main grid connect and exchange power simultaneously. In addition, the impact of hydrogen stations due to the growth of hydrogen vehicles and their crucial role in the power system's future to reduce pollution is also discussed. Moreover, the effect of time-based demand response programs (TOU) according to the elasticity matrix (different operators and price-sensitive flexible loads) for the proposed multilateral grid is investigated under diverse scenarios. Stochastic planning is performed to make results more realistic and authentic. The uncertain parameters for stochastic planning include wind pace, solar radiation, fuel rate, and various demands. The assumed triple objective functions for the proposed planning are the microgrid's profit, the nano-grid's cost, and the total multilateral grid's pollution. The problem is modeled as mixed-integer linear programming (MILP) and solved using the GAMS and LP metric approach. The final results show that by implementing the supposed planning, the microgrid's profit increases by about 22.53 $/day (10.8%), and the nano-grid's cost decreases by about 1.31 $/day (9.8%). On the other hand, the total environmental pollution is reduced significantly and reaches 1.06 kg/day.

A smart building energy management incorporating clustering-based tariffs in the presence of domestic solar energy, battery, and electric vehicle

Smart buildings play a crucial role in optimizing energy management within the power network. As end-users of the power network, they have the ability to not only reduce economic costs for householders but also modify the technical indices of the power network. To promote efficient device management in smart homes (SH), demand response programs are recommended for consumers. This research investigates the application of clustering-based electricity pricing strategy aimed at effectively managing the energy devices of a residential smart home. The utilized method categorizes the electricity tariff into five rates according to the clustering of the real-time pricing program. Ward’s clustering method is utilized to cluster and determine new electricity tariffs. The primary goal of the energy management program is to minimize the building’s energy cost, which is accomplished through the utilization of the multi-verse optimizer. The smart home consists of essential and manageable appliances, a photovoltaic panel (PV), a sodium-sulfur (NaS) battery, and an electric vehicle (EV). The initial parameters of the PV and EV are modeled stochastically by their probability distribution functions and calculated using the Latin hypercube sampling algorithm. The smart building’s performance is assessed by taking into account various demand response programs. The numerical results present that the application of the clustering-based management method has resulted in a significant reduction of 23–43 % in the electricity cost of smart homes. Additionally, the smart home exhibits a more linear consumption pattern when considering the electricity tariffs based on the clustering approach.

Customer enrollment and participation in building demand management programs: A review of key factors

Increasing the efficiency and flexibility of electricity demand is necessary for ensuring a cost-effective and reliable transition to zero-carbon electricity systems. Such demand-side management (DSM) resources have been procured by utilities for decades via energy efficiency and demand response programs; however, the key drivers of program enrollment and customer participation levels remain poorly understood — even as governments and grid planners seek to scale up the deployment of DSM assets to meet climate targets. Here we systematically review the evidence on multiple factors that may influence customer enrollment and participation in building DSM programs, focusing primarily on residential and commercial buildings. We examine the contexts in which relationships between DSM factors and outcomes are most often explored and with which methods; we also score the strength, direction, and internal consistency of each factor's reported impact on the enrollment and participation outcomes. We find that studies most commonly assess the effects of economic incentives for load flexibility on program participation levels, often using simulation-based methods in lieu of measured data. Few studies focus on program enrollment outcomes or regulatory drivers of either enrollment or participation, and gaps are also evident in the coverage of emerging DSM opportunities like load electrification. Removal of structural barriers (e.g., the lack of controls infrastructure) and the use of third party services (e.g., load aggregators) are the factors with the largest positive impacts on DSM outcomes, but no single factor emerges as clearly most impactful. For a given factor, the range of reported impacts typically varies widely across the relevant studies reviewed. Our findings provide a snapshot of the state of knowledge about building DSM and customer decision-making, and they expose key gaps in understanding that must be filled if building DSM is to expand as a critical resource for operating clean power grids.

The Effect of Smart Grid Technology on Energy Consumption Management in Canada

Purpose: The aim of the study was to evaluate the effect of smart grid technology on energy consumption management in Canada. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Smart grid technology has significantly enhanced energy consumption management in Canada. By integrating advanced metering infrastructure and real-time data analytics, smart grids optimize energy distribution, reduce peak demand, and enhance grid reliability. This technology enables better demand-response programs and promotes renewable energy integration, leading to more efficient energy use and reduced environmental impact. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & the theory of planned behavior (TPB) may be used to anchor future studies on the effect of smart grid technology on energy consumption management in Canada. Smart grid technology enables more efficient energy usage by providing consumers with real-time data and insights into their consumption patterns. The vast amounts of data generated by smart grids necessitate robust policies to protect consumer privacy and ensure data security. Policymakers should establish clear regulations governing the collection, storage, and use of smart grid data.

Optimization of microgrid operations using renewable energy sources

Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. Keywords: Optimization, Microgrid, Operations, Renewable Energy, Energy Sources.

Internet of things and deep learning-enhanced monitoring for energy efficiency in older buildings

Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older buildings contribute greatly to energy waste since they typically lack new energy-efficient technology. Reducing carbon emissions, lowering energy bills, and extending the life of these historic landmarks all depend on fixing the energy inefficiency that plagues these buildings. Despite advanced technologies' remarkable progress, the potential of the Internet of Things and deep learning in older buildings has not been unexplored. Major obstacles include expensive and out-of-date infrastructure and the difficulty of incorporating new technology into historically significant structures. Existing research has mostly ignored older infrastructures' unique requirements and limitations in favour of current or newly built services. In addition, comprehensive research on integrating deep learning with the Internet of Things in this specific environment has been lacking. Smart building energy management is made possible by the Internet of Things (IoT) and deep learning. Architectural limitations, outmoded infrastructure, and the necessity for non-invasive retrofitting solutions all contribute to the difficulty of energy monitoring and improvement in older buildings. This research proposes combining the IoT with Deep Learning-enhanced Predictive Energy Modeling (DL-PEM) to make an energy management system that can change and adapt to the needs of older buildings. Data from IoT sensors is collected on occupancy, temperature, lighting, and equipment usage and then analyzed using Deep Learning models to determine the most efficient energy consumption patterns. Beyond its energy-saving potential, this method has many potential uses. Spotting and fixing structural problems before they become major can improve occupant comfort, reduce maintenance costs, and pave the way for predictive maintenance. Integration with the Smart grid and demand response programs can be facilitated, too, improving the reliability of the power system as a whole. Our IoT and Deep Learning-based energy management solution optimizes energy usage, reduces expenses, and mitigates environmental impact in older buildings, as shown by extensive simulation studies. The system's performance is compared to more conventional methods, and its flexibility is evaluated in various building and usage contexts. The experimental outcomes show the suggested DL-PEM model increases the energy demand forecasting analysis, energy consumption analysis, thermal comfort optimization analysis, seasonal variation analysis, and occupancy data analysis.

Distributionally robust CVaR optimization for resilient distribution system planning with consideration for long-term and short-term uncertainties

Climate change is exerting excessive strain on the environment, leading to an ever-increasing frequency of extreme weather events that negatively impact distribution systems. To mitigate these impacts, this paper presents a distributionally robust conditional value-at-risk (DR-CVaR) optimization approach for resilience-oriented distribution system planning. Firstly, a resilience-oriented distribution system planning model is developed, which incorporates flexible operational resources, such as mobile energy storage prepositioning and demand response program, in the emergency response phase. Then, the uncertainties associated with extreme weather events are addressed using a DR-CVaR optimization approach, where the short-term uncertainty in the occurrence of outages caused by specific extreme weather events is addressed through a distributionally robust optimization approach, while the long-term uncertainty regarding the risk of potential extreme weather events is addressed by a conditional value-at-risk optimization approach. The DR-CVaR model is eventually transformed into an equivalent three-level model and solved using a customized nested column-and-constraint generation algorithm. Finally, case studies are conducted based on the IEEE 33-bus distribution system under several extreme weather events. The numerical results show that the proposed model can reduce the load shedding cost as well as investment cost, which confirms the effectiveness and superiority of the proposed model.

Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Optimal operation of multi-carrier energy systems considering demand response: A hybrid scenario-based/IGDT uncertainty method

The optimal operation of multi-carrier energy systems (MCESs) has opened new horizons for energy network management and the satisfaction of consumers. In this paper, the optimization of the MCES's operation cost is considered by combining several energy hubs (EHs). To make optimal use of thermal and electrical demand response programs (TDRPs and EDRPs), the cost factors are extracted using a new sensitivity analysis (SA) method. Then, taking into account the optimized cost coefficients in EDRP and TDRP, the optimal operation of MCES is investigated in the presence of uncertain parameters. In this paper, a two-stage uncertainty method is used for solving the uncertain problem. First, the uncertainty of wind, photovoltaic (PV), and electrical and thermal loads is modeled with a scenario-based method, in the second step, the price uncertainty of generated electricity is added to the previous model using the information gap decision theory (IGDT). A hybrid scenario-based/IGDT uncertainty has been performed simultaneously at the GAMS platform. The results show the impact of each uncertainty parameter and system resilience due to the optimization done in the problem and the EDRP and TDRP programs. The results show about 135$ difference between the lowest and highest scenarios at a total operation cost.

An integrated price- and incentive-based demand response program for smart residential buildings: A robust multi-objective model

Residential buildings consume a significant amount of energy, emphasizing the importance of optimizing energy usage. Demand-side management (DSM) helps consumers and producers manage energy consumption through incentives and pricing. This study develops a new mathematical model to manage DSM in smart residential buildings. Extant literature commonly considers only a single objective function, ignores uncertainties, and applies only one price- or incentive-based program to load management in smart residential buildings. This study develops a multi-objective mixed-integer linear programming (MILP) model that applies both price- and incentive-based programs and considers uncertainties. The objectives are cost reduction, peak load minimization, user comfort improvement, and load factor maximization. This model can manage optimal schedules for household appliances and power exchange within buildings. The study shows that participating in the incentive-based program in a four-household residential complex yielded a 2 % decrease in electricity costs and a 1 % reduction in peak load while upholding comfort and load factor levels compared to non-participation. When extended to an eight-household complex, potential benefits include an 8.3 % decrease in electricity cost and a 2.6 % reduction in peak load, highlighting the program’s effectiveness in residential energy management strategies.

Enhancing low-carbon building operations leveraging demand response driven by renewable energy consumption contributions

In the pursuit of sustainable low-carbon energy practices, demand response (DR) has emerged as a pivotal strategy, particularly in the management of building energy consumption. However, current demand management methods often lack a thorough evaluation of building carbon reduction benefits and fall short in promoting grid decarbonization. This paper proposes a low-carbon operation optimization method for buildings, with a novel DR strategy aimed at facilitating local renewable energy consumption and system-level renewables integration. Considering local and systemic renewable shares, an evaluation framework is first introduced for quantitative building energy assessment in each period. The building loads with controllable flexibilities are then grouped and aggregated based on their elasticity characteristics and probability distribution. Finally, a low-carbon optimization model is established, leveraging the DR strategy for sustainable building operations through flexible load regulation. Utilizing measured building data in Shanghai, a case study with extensive analysis validates the effectiveness of the methodology. The results suggest that following DR implementation, the similarity between the building load profile and the systemic renewable share improves by 34.48 %. Moreover, carbon emissions decrease by 5.27 % at the same level of building energy consumption. Further analysis highlights that energy storage can enhance load adjustment capabilities and the potential for reducing carbon emissions. Compared to conventional DR programs, the proposed strategy demonstrates significant advantages of abundant flexibility and low-carbon benefits. The widespread implementation of low-carbon operations in large-scale building groups has profound implications for integrating renewable energy and achieving system decarbonization goals.© 2017 Elsevier Inc. All rights reserved.

Conceptual design and optimization of integrating renewable energy sources with hydrogen energy storage capabilities

Systems are inherently tied to uncertainty, which necessitates the development of designs and schedules that accommodate this unpredictability. Particularly in modern power systems, the variable efficiency of numerous factors, including pricing, underscores the need for uncertainty modeling. In the meantime, the incorporation of EVs (electric vehicles) into the electrical grid is advancing, with these vehicles being recognized for their role in reducing dependency on fossil energies and enhancing the resilience, stability and efficiency of the grid. However, the rapid growth of the EV sector necessitates thoughtful strategies from decision-makers to manage its expansion effectively, particularly considering the environmental implications associated with the life cycle of EVs, including their charging and discharging processes. This research introduces the conceptual design and optimization approach utilizing a hybrid bat algorithm and differential evolution algorithm (BA-DEA) to improve the efficiency and resilience of EV-smart parking lots under the unpredictable conditions caused by grid pricing fluctuations within the DRP (demand response program). The technique effectively modulates daily costs by adjusting loads among peak and off-peak periods. Key features of the proposed approach include a non-dominated sorting model, innovative variable identification, a memory-based selection process, and the application of fuzzy theory to identify optimal Pareto outcomes. This methodology is not only swift in converging to a solution but also demonstrates a high likelihood of reaching the global optimum. Modeling considerations for smart hydrogen storage systems (SHSSs) incorporate significant constraints, notably those associated with electrolyzers, fuel cells, and storage capacities. The algorithm's effectiveness is validated in scenarios involving parking facilities and multiple uncertain resources, demonstrating a robust reduction in specific cost indicators and adjustments in cost dynamics when DRP is considered. Specifically, the algorithm achieves a substantial 42% decrease in cost variability when DRP is excluded, and a 46.9% reduction in cost variability when DRP is included, despite a minor 5% increase in average cost. These outcomes underscore the proposed system's capability to improve resilience and thermal performance under fluctuating conditions, making it a promising solution for future smart energy systems. Further, the results exhibited the complex interplay between cost optimization and operational adjustments in response to demand-side management.

Securing demand–response in smart grids against false pricing attacks

Two-way communication systems in smart grids help to engage consumers in demand–response programs, which can bring many benefits but also make smart grids vulnerable to cyber attacks. In this paper, a cyber attack which aims to disturb the demand–response process by injecting false electricity price signals is considered. A real-time pricing model where the operator has incomplete knowledge of the private demand–response behaviors of the customers is proposed. The vulnerability of the power system to false pricing attacks is analyzed by a Markov decision process, and the dynamic interaction between the attacker and the defender is modeled as a zero-sum Markov game where neither player has full information of the game model. For the solution of the Markov game, a model-free multi-agent reinforcement learning method is proposed to find the Nash Equilibrium policies for both players. The proposed method is applied to the IEEE 34 Node Test Feeder, in which the effect of the defense on mitigating the impact of the attack is demonstrated and different policies of the players given various resources are analyzed. The results shows that the studied cyber attack can cause a maximum of 1.96% unsatisfied load and the proposed defense measure can reduce the unsatisfied load by 50%–70% compared with the cases without defense measures.

Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations

As renewable energy expands, its fluctuations challenge grid security. Demand response (DR) is a crucial solution for ensuring grid stability. This paper designs a two-stage optimization model for the retail electricity provider (REP), aiming to develop differentiated prices to incentivize multiple types of users in DR. In the first stage, the REP utilizes energy storage to adjust electricity purchases. In the second stage, a bi-layer optimization model is constructed. The REP participates in incentive-based DR and sets differentiated prices for lower-level users. Based on these prices, multiple types of users modify their electricity consumption behavior, aiming to maximize the combined satisfaction with the comfort and economy of electricity consumption. Finally, the Karush-Kuhn-Tucker (KKT) method is used to derive the equilibrium solution between user response quantity and price. The results indicate that (1) Combining energy storage and user-side DR boosts responsiveness by 28.5% compared to storage alone and 23.2% compared to user-side DR alone. (2) Energy storage allows the REP to create more compelling prices for residential users. (3) Differentiated pricing boosts industrial users participating in DR, resulting in over 50% higher response rate than uniform pricing. This study provides policy implications for grid managers to develop effective DR programs.

Renewable Revolution: A Review of Strategic Flexibility in Future Power Systems

The global shift toward integrating renewable energy sources, such as solar and wind power, is revolutionizing the energy landscape, presenting both opportunities and challenges for power system operations. This paper delves into the critical concept of power system flexibility, emphasizing the importance of maintaining reliable service amidst unpredictable fluctuations in supply and demand. Given the intermittent nature of renewable energy, this paper explores key aspects of flexibility, including generation, demand-side, and grid flexibility, and their roles in sustaining grid stability. Various strategies to enhance flexibility in future power networks are examined, such as advanced energy storage technologies, demand response programs, grid expansion and interconnection, sophisticated forecasting methods, and adaptive power generation. Additionally, this paper underscores the pivotal role of market and regulatory reforms in facilitating the integration of renewable energy sources and enhancing grid adaptability. By analyzing the benefits, limitations, and future prospects of each strategy, this work provides a comprehensive overview of the evolving energy sector. It highlights the necessity of aligning regulatory frameworks with sustainability and climate goals to navigate the complexities of this transition effectively.

A pool-based electricity retail markets integrating renewable energy sources and electric vehicles in the presence of demand response program and conditional value at risk to enhance energy security

In restructured power systems, energy security has become a critical concern, driven by changes in laws and regulations and underscored by a defensive approach implemented by the independent system operator. This alignment highlights a strong interdependence between energy security, energy supply, and the economic aspects of energy management. The paper explores how consumers energy supply in restructured systems can be managed through Demand-Side Management (DSM) and Demand Response (DR) programs, considering economic efficiency and risk in scenarios that include Renewable Energy Sources (RES), suitable storage systems, and electric vehicles (EVs). An energy security index is introduced to evaluate the impact of DR programs on both electrical and thermal energy, considering the economic risks involved. A hybrid pool-based electricity retail market approach is utilized to optimize energy security, supply, and consumption. The significant roles of RES, storage systems, and EVs are highlighted in this context. Economic and environmental optimization is achieved using the Conditional Value at Risk (CVaR) method, facilitating strategic decision-making under varied risk conditions. The energy supply security is further analyzed in the presence of electricity market retailers, exploring different pricing strategies. A Mixed-Integer Programming (MIP) model, implemented in GAMS software with the CONOPT solver, is used to examine pricing methods such as Time-of-Use (TOU) and real-time pricing. Findings reveal that DR programs lead to substantial increases in retailer profit and energy supply security value, with real-time pricing showing a significant 23.5 % increase in energy supply security value and a 24.5 % increase in retailer profit compared to fixed tariff pricing.

Optimal scheduling of a community Multi-Energy system in energy and flexible ramp markets considering Vector-Coupling storage Devices: A hybrid Fuzzy-IGDT/Stochastic/Robust optimization framework

Community multi-energy systems (CMESs) are emerging as a pivotal solution to address the pressing issues of the current energy crisis and environmental pollution. This paper introduces a comprehensive multi-horizon risk-averse scheduling strategy for CMESs, targeting participation in energy and flexible ramp markets while meeting the demands of electric, natural gas, and hydrogen vehicle charging stations, data centers, and residential and commercial buildings. The proposed CMES incorporates power-to-x vector coupling storage (PtX-VCS) systems, enabling the storage of electricity in various forms to cater to multi-energy demands. Additionally, an integrated demand response (IDR) program, incorporating incentive and load-shifting strategies, is applied to enhance operational efficiency and system flexibility. To manage uncertainties effectively, a hybrid stochastic/robust/fuzzy information gap decision theory (IGDT) method is employed. This approach, formulated as a tri-level optimization model, is transformed into a single-level model using strong duality theory. The results demonstrate a notable reduction in daily operational costs by approximately 5% and 4% through the implementation of the IDR program and PtX-VCS technologies, respectively. Moreover, the CMES’s potential to participate in the flexible ramping market leads to an approximately 7% reduction in total operational costs.

Different Demand Response Programs with its Implementation in Various Countries and the Role of TOU DR in the Context of Nepal

With the increasing trend of electricity consumption worldwide, it creates a cumbersome, reliable power system and necessitates the construction of new generator companies, transmission lines, and other infrastructures. Despite investing in new infrastructures to manage occasional peaks, utilities can use the demand response concept to effectively handle demand during such peak time intervals, and in doing so, it enhance the grid stability. This assists in balancing the supply and demand for electricity, making the grid more resilient to fluctuations, and reducing the risk of blackouts. This paper delves into the diverse DR programs across different countries and comparative analysis for finding the suitable DR method to make a more reliable electric power network in Nepal.

Alternating direction method of multipliers based distributed energy scheduling of grid connected microgrid by considering the demand response

Global warming, environmental degradation, clean energy production, intermittent, volatile, and unpredictable renewable energy sources (RES’s), occasional peak demand on the system necessitates energy management (EM). Demand response (DR) programs in the distribution network can be seen as one of the foundation stones in the future of EM. This article illustrates the need for EM using DR, its benefits, types of loads, clustering techniques, price-based demand response (PBDR) etc. To accomplish the EM goals and to attain the economic benefit, DR employs peak shifting, peak clipping, valley filling and load growth. However, the accumulation of large loads at low electricity prices creates local peaks, this phenomenon is referred to as payback or rebound effect (RE). The occurrence of RE at low price zone heightens the volatility of market clearing price (MCP) and the operational cost of the microgrid. Inherently, the scheduled inelastic consumers at low price zone suffer from increased MCP and therefore, the total consumer tariff (TCT). The occurrence of RE depends on the load curve, peak to average ratio, electricity price and the percentage of interruptible loads present in the system. Unclear pricing methods impede the participation of customers in DR events. Moreover, majority of techniques presented in literature are of centralized frameworks that needs complex communication technologies. To fill these glitches the proposed work uses a simple distributed scheduling approach based on alternating direction method of multipliers (ADMM) to alleviate the energy management using an IEEE-18 bus system. The load factor increases from 0.79 to 0.83. Using DR lowers the peak power demand on the MG from 82 to 78 kW without compromising customer comfort or satisfaction. The TCT was lowered from scenario 1 to scenario 4 from 3058 to 2254 euros. The system's average demand dropped from 65.54 kW to 64.8 kW. IEEE-33 bus system was considered to assess the impact of RE on the MCP and TCT. Additionally, the marginal generator provides 72.51 kW of electricity in sub case 3 and 166.26 kW of power in sub case 2. Due to a decrease in power dispatch from the marginal generator, TCT increased from sub case 2 to sun case 3 by 11,046.41 rupees to 12,912.75 rupees. In contrast, TOC decreased from 6495.45 rupees to 6150.75 rupees from sub case 2 to sun case 3.