Price-based Demand Response Programs Research Articles

General guidelines for the optimal economic aggregation of prosumers in energy communities

Energy communities are regulatory tools promoting aggregations of users to foster the shift towards a renewable distributed generation. First in the literature, this paper addresses together three main aspects affecting the convenience of these aggregations: the complementarity between generation and demand of different prosumers, the criterion allocating the operating costs of energy communities, and the application of demand-response programs. The goal is quantifying the relative weight of these aspects using Mixed-Integer Linear Programming to minimize the operating costs of citizen and renewable energy communities, where prosumers are connected to the grid as single entity, or separately. Incentive- or price-based demand-response programs and a novel cost allocation criterion, which rewards the members with the highest economic benefit in passing from simple consumers to prosumers, are applied to each community configuration. Results allow identifying general guidelines for the optimal economic operation of energy communities: i) complementarity may reduce costs by 15–20%, ii) a fairer cost allocation criterion may reduce the bills of prosumers using free-of-charge renewables by 20–30% compared to those using dispatchable sources, and iii) price-based demand-response may reduce community costs beyond 50%. Eventually, directions of further research, as the impact of energy communities on a national power system, are drawn.

A sustainable framework for long-term planning of the smart energy hub in the presence of renewable energy sources, energy storage systems and demand response program

Energy hub systems are known as the most important local energy systems and simultaneously meet the needs of their customers for different energies. Optimal design of energy hub systems has become a very important issue, as these systems have many customers. So, this paper presents a sustainable framework for the energy hub long-term planning in the presence of renewable energy sources (RESs). The hub is capable of supplying electrical, cooling and heating loads and is also equipped with three storage systems, electrical, thermal and cooling storage units. The uncertainties of different loads as well as photovoltaic (PV) panel output power are considered and the planning problem is dynamically modeled for a 15-year horizon. In addition, a Price-Based Demand Response (PBDR) program is included in the proposed model so that hub consumers can participate in it, leading to a modification of the demand curve and a reduction in operating cost. The planning and operation problem is modeled as a mixed-integer linear programming (MILP) problem and solved by the CPLEX solver in GAMS software. The effects of PBDR program, PV panels, uncertainties and energy storage systems on the sizing of equipment and operating costs have been studied in detail. The results show that the use of PBDR program reduces the installed capacity of the electrical storage system (EES) and thus leads to a reduction of about 5% of investment costs. In addition, the results show that the absence of PV panels in the model increases the operation costs by about 9%.

A novel approach for incorporating incentive-based and price-based demand response programs in long-term generation investment planning

Today, power system planning requires simultaneous attention to supply and demand side management. It has gained remarkable attention due to the advent of new technological paradigms such as smart grid concepts and renewable energy resources. In this study, the effects of Demand Response (DR) as an emerging strategy of demand side management have been investigated on generation investment planning for achieving a technical, flexible, economical, and reliable solution for the supply side in the long-term. To this end, taking the advantage of system dynamics concepts and tools, the modeling of both principal DR types, i.e., price-based and incentive-based, is explored. In the first type, we formulate a novel market-based mutual relevance between market clearing price and willingness to pay of the price-based loads for accurately obtaining the electricity price as the primary signal for the investment process. The uncertain behavior of loads in these programs is expressed with a new concept called Certainty of Responsiveness (COR). A novel index, namely Loss of Load Duration Margin (LM), is proposed to incentivize the participants in incentive-based DR. Dispatching of these loads has been done using options contract. Numerical simulations are performed on a typical test supply system. Wind and solar photovoltaic units as the leading renewables in power systems are considered alongside the conventional thermal technologies. Different regulatory decisions about the adjustable parameters of DR programs are also analyzed. The results validate the effects of DR programs on total installed generation capacity, market prices, reliability indices, and outage costs in the long-term.

Impact of implementing a price-based demand response program on the system reliability in security-constrained unit commitment problem coupled with wind farms in the presence of contingencies

Large scale penetration of highly intermittent wind energy sources has caused new challenges to the reliable operation of power systems. To address these challenges, demand response program is considered as an efficient solution to manage shift-able loads. In this paper, the impact of demand response on power system reliability has been evaluated by developing a two-stage stochastic security constraint unit commitment that considers both the wind volatility and line outages at the same time. The allocation of ramping products and spinning reserves to cover these uncertainties has also been modeled. wind curtailment and load shedding are considered as alternative strategies, and an optimal exchange between them and the use of spinning reserves and flexible ramping products has been modeled in the objective function. To assess the system reliability, the expected load not served (ELNS) index was evaluated in different cases. The proposed scheme as a mixed-integer linear programming problem was solved by a scenario-based two-stage stochastic programming method. The IEEE test systems 6-bus, modified 24-bus, and 118-bus were examined numerically. The results show that the simultaneous consideration of demand response and flexible ramping product coordinated with wind energy has adequate flexibility to reduce the reliability index and system operational cost.

A multi-criteria approach to optimize the design-operation of Energy Communities considering economic-environmental objectives and demand side management

This paper focuses on three different configurations of energy communities (ECs) modelled as aggregations of local prosumers of renewable electric and thermal energy. The goal consists in improving the economic performance of the ECs while contributing to address the issue of climate change and fulfilling the existing energy demands or demands modified with smart strategies. Accordingly, type and size of the energy conversion and storage units of the prosumers included into ECs (design) and their operation are optimized with a bi-objective approach, considering investment and operation costs, and greenhouse gas emissions, both direct due to fuel burning, and indirect due to life cycle, as objectives to minimize. Moreover, two strategies of demand side management (DSM) are considered: a price-based demand response program applied downstream of the design optimization, and a new DSM model, which adapts the electricity demand to the renewable energy sources locally available, applied upstream of the design optimization. It results that the proposed DSM can ensure a better balancing between generation and demand profiles, thereby decreasing the stress on the electricity grid. Globally, ECs can reduce their energy expenditure of 14% and the overall CO2-equivalent emissions of 24% compared to the reference case of the simple consumers.

Demand Response Aggregation With Operating Envelope Based on Data-Driven State Estimation and Sensitivity Function Signals

With the increasing penetration of renewable energy sources (RES), the value of the demand response (DR) draws wide attention. In order to realize the coordinated dispatch of widely spread resources, the aggregation of the controllable residential loads is managed by a single entity, namely the DR aggregator. Under the price-based DR programs, the DR aggregators actively respond to the market signals to reach maximum welfare. To avoid the quality of electricity services being jeopardized, the operational constraints of the network should be considered by the DR aggregators. However, DR aggregators are not expected to have access to the monitoring equipment and have limited knowledge of the network states. Hence, in this paper, we proposed a DR aggregation with the operating envelope framework based on the representative signals produced by the distributed network operator (DNO) in the context of big data era. The DNO provides representative signals, including real-time state estimation and sensitivity functions, to the DR aggregators based on the proposed Semi-supervised Coupled Generative Adversarial Imputation Network (SC-GAIN) and big data analysis. The DR aggregators can realize the secure and efficient real-time dispatch of the controllable loads based on the received signals. The proposed framework was verified on the IEEE 33-bus and 123-bus systems. The case studies show that the proposed SC-GAIN algorithm can better deal with the missing data, and the learned sensitivity functions can effectively avoid the overestimation of the true DR potential.

Pool trading model within a local energy community considering flexible loads, photovoltaic generation and energy storage systems

This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers’ willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.

Optimal energy management of multi-carrier networked energy hubs considering efficient integration of demand response and electrical vehicles: A cooperative energy management framework

This paper presents a cooperative energy management approach for multi-carrier networked energy hubs. The proposed scheme facilitates energy hubs to exchange power for higher economic and environmental benefits. Based on the cooperative game theory, individual energy hubs cooperate to secure maximum profit, which is then fairly distributed among the hubs to ensure the economic stability of the coalition. The proposed model considers the networked system in which hubs are integrated with multi-energy resources such as electrical and natural gas supply, renewable sources, combined heat and power units (CHPs), gas boilers, electrical and thermal energy storage units to satisfy electrical and thermal demands. A scenario-based generation and reduction algorithm is employed to address the uncertain behaviour of sources. Moreover, a price-based demand response program (DR) and electrical vehicles (EVs) are integrated to make the network more flexible. To analyse the proposed cooperative energy management, the system is modelled as a mixed-integer linear programming problem. Results show that operating costs and greenhouse gas (GHG) emissions are reduced and the self-reliability of the networked system improves. With cooperation and flexible resources, the coalition achieves its maximum profit, which is fairly and stably distributed among the hubs by using Shapley value.

A non-linear resilient-oriented planning of the energy hub with integration of energy storage systems and flexible loads

This paper develops a two-stage model for planning of the hub, taking into account various uncertainties, in which the sizing of hub converters is performed with the aim of improving hub resilience. In the first stage, the capacity of hub converters is selected and in the second stage, the operation problem is solved under N-1 contingency conditions. In the proposed model, Price-based demand response (DR) and direct load control (DLC) programs are considered to implement in normal and emergency situations, respectively. The robust optimization method is utilized to deal with the uncertainties the problem is formulated as a mixed-integer non-linear programming (MINLP) problem. Finally, DICOPT solver in GAMS environment is used to solve the model and the results demonstrate that considering the N-1 contingency conditions in the design problem leads to the installation of more converters as well as larger capacities. The results also indicate that the operating cost of the hub during the outage condition of combined heat and power (CHP) is reduced by 63.48% through the implementation of the DLC program considering energy storage system. Overall, the results illustrate that considering the emergency operating conditions in the hub design problem, leads to a significant enhancement in its resilience.

A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid

Uncontrolled electric vehicle (EV) charging is known to have an adverse impact on power networks. In particular, the inherent problem of phase load unbalance (PLU), due to uneven loading of phases can be substantially increased due to uncoordinated EV charging demands. Increased unbalance would cause sub-optimal operation of the distribution grid with higher thermal losses and increased network congestion. In this paper, a day-ahead EV scheduling strategy to mitigate unbalance is proposed, by controlling single-phase charging demand of EVs with vehicle to grid (V2G) power transfer. In addition, the EVs are also scheduled as per a price-based demand response program. The aim of this work is to use multi-objective lexicographic ordering to achieve economic benefit by controlling EV charging/discharging rates as per dynamic electricity prices, while simultaneously shift EV power consumption among phases to reduce unbalance. Further, uncertainty handling optimization techniques are presented to hedge against any randomness in prices and phase-wise loads. The proposed methods are simulated for a community charging station connected to the unbalanced grid. It is found that the active power consumption among phases can be balanced almost entirely using continuous charging/discharging rates from EVs. Moreover, up to 30% reduction in charging costs can be achieved while maintaining a balanced three-phase system.

Bi-level retail pricing scheme considering price-based demand response of multi-energy buildings

Multi-energy buildings can help the consumers to achieve more efficient energy consumption and offer more flexible demand response. However, since the demand response and retail pricing affect each other, an equilibrium based retail pricing scheme should be developed where the energy costs of the consumers can be reduced and more profits can also be earned by the retailer at the same time. Therefore, this paper proposes an optimal bi-level retail pricing scheme for the retailer and consumers of the multi-energy buildings to perform the price-based demand response program. In the lower-level, the optimal building energy management model considering the thermal dynamic and multi-energy conversion is established to minimize the energy costs, which ensures the benefits of consumers and also represents the dynamic response characteristic of the multi-energy building; in the upper-level, the optimal pricing strategy of the retailer is modelled to maximize its profit, and the stochastic AC power flow is applied to deal with the uncertain output of the renewable energy sources. By reforming the bi-level structure, linearizing the price-load coupled objective, and relaxing the power flow with the second-order cone, the modified market equilibrium model is solved by the Dantzig-Wolfe decomposition in a distributed way, which can protect the privacy of different players and deal with the undifferentiable integer variables caused by the reformulation. The simulation results demonstrate the efficacy of the proposed retailing pricing scheme and distributed solution algorithm.

An Advanced Satisfaction-Based Home Energy Management System Using Deep Reinforcement Learning

Home energy management (HEM) systems optimize electricity demand of appliances according to the price-based demand response (DR) programs. Undoubtedly, customer satisfaction is of such importance that if not taken into consideration, it prevents customers from participating in the DR. HEM systems suffer from high nonlinearity due to the variety of smart appliances and different criteria for customer satisfaction. In this paper, an advanced satisfaction-based HEM system using deep reinforcement learning is proposed to hourly schedule the controllable and time-shiftable appliances, including electric vehicle, air conditioner, and lighting system as controllable loads and washing machine, and dishwasher as time-shiftable loads. The proposed framework deploys a Deep Q-Network (DQN) method. Regarding customer dissatisfaction, this paper takes into consideration nonlinear precise functions. The Kano model for EV departure SoC, charging duration and lighting system satisfaction, desired temperature span for air conditioner, and the desirable operation period, waiting time, and consecutive mode of dishwasher and washing machine are taken into account. The proposed HEM system is applied to a smart home, and the results are compared with those of the Q-Learning algorithm. Numerical results prove the effectiveness of the proposed HEM system in reducing electricity cost and customer dissatisfaction, as well as the superiority of DQN over Q-Learning as well.

IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.

Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers

This paper designs and models a fully decentralized peer-to-peer (P2P) energy token market for small-scale prosumers using blockchain technology in a smart grid environment in the presence of the demand response (DR) program and demurrage mechanism. As the market players, prosumers in the local distribution network are considered in two groups, producers (sellers) and consumers (buyers). Using smart contracts, all sellers and buyers in the proposed market can engage in bilateral energy token transactions (P2P) with each other under an agreed price and with the retail market at a certain price. Furthermore, the local consumers can participate in price-based DR programs and shift their consuming load to the periods with high local generation. Demurrage mechanism is applied to avoid energy token accumulation and enhance attraction for local transactions. With demurrage in place, the redemption value of energy-backed tokens reduces with time. A fully decentralized approach called the primal-dual sub-gradient method is developed to clear this fully decentralized energy market in the presence of DR programs and demurrage. The proposed market-clearing scheme guarantees the global and feasible solution without requiring the players' private information. Numerical studies demonstrated the feasibility and effectiveness of the proposed energy token market and the decentralized approach for its clearing.

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

Smart energy hub optimization in presence of stochastically modeled renewables and loads

Integration of information technology with multi-carrier energy systems has paved the way for smart energy hubs (SEHs). Such energy hubs require stochastic modeling of intermittent renewable energy resources (RERs) and fluctuating demands to realize their optimal operation. Many excellent works are available related to their optimization but none of the existing works presents a comprehensive model which not only incorporates the stochastic nature of RERs and demands but also taps demand response potential in the presence of system storages. To fill this gap, this paper proposes a comprehensive model for an SEH which incorporates the stochastic nature of electrical, heating, and cooling demands in the presence of RERs, batteries, and thermal storages. In addition, optimal shifting of electrical, heating, and cooling loads are performed to further reduce the operational cost of SEH considering a price-based demand response program (DRP). The cost minimization objective is formulated as a mixed-integer linear programming (MILP) optimization problem. Five different cases are analyzed considering the inclusion and exclusion of storage and RERs with different percentages of shiftable loads for DRP. The proposed complex model is solved using CPLEX in GAMS environment. With RERs, cost reductions of 5.25% and 18.96% are observed in the operational cost of SEH without tapping demand response (DR) potential. When 10% and 20% loads are deemed to be shiftable for DR, operational costs reduce to 20.15% and 21.43% respectively with RERs and energy storages. Results of the study can support energy managers in the optimal operation of multi-carrier energy systems.

Aligning the interests of prosumers and utilities through a two-step demand-response approach

Demand-side management solutions reward flexible customers for achieving desired goals. However, under price-based demand-response programs, uncoordinated load shifting among many customers may lead to rebound peaks, thus incurring financial costs on the energy service provider (ESP). This study addresses this issue from both the ESP (utility company) and customer’s perspectives in a two-step approach. First, each customer solves a local multi-objective load scheduling problem. Thereafter, the ESP solves a system-wide demand profile optimization problem. More precisely, we formulate in the first step a multi-objective optimization model to minimize consumption costs and load schedule discomfort while considering multiple energy sources and customer preferences. In the second step, we design an approach that combines Pareto-optimal solutions from all customers and minimizes their aggregate demand profile’s peak-to-average ratio (PAR). Experimental results show that the proposed approach outperforms the equivalent single-objective optimization models, and it can reduce the PAR metric by up to 11%. In other words, the proposed approach successfully improves the ESP’s system-wide demand profile aggregation while reducing customers’ expenses and discomfort.

Estimating impact of price-based demand response in contemporary distribution systems

Price-based Demand Response (PBDR) programs have been envisaged to motivate customers for changing their consumption patterns by reducing peak demand against financial gains. DR is an emerging strategy to deal with peak demand, energy management, line congestion, reliability, security and economy of contemporary distribution systems. Therefore, it becomes essential to assess overall impact of DR prior to its implementation. However, the job is quite tedious as demand of the customers also depends upon socio-economic factors, human psychology, ambient conditions, etc., besides the dynamic price signal. This paper proposes a comprehensive, more realistic, simple and adaptive mean price-based DR modelling to assess overall impact of PBDR while addressing stochastic demand, and price elasticity of demand (PED) for individual customer. The model suggests dynamic PED to capture the stochastic DR of customers against real-time price signal using historical data. In addition, several DR indices are suggested to assess the impact of DR on several stakeholders. The study on a standard test bench reveals that proposed methodology is simple, robust, quick, and can deliver tangible information in the interest of concerned stakeholders.

Energy trading and control of islanded DC microgrid using multi-agent systems

The energy sector is experiencing a revolution that is fuelled by a multitude of factors. Among them are the aging grid system, the need for cleaner energy and the increasing demands on energy sector. The demand-response program is an advanced feature in smart grid that strives to match suppliers to their demands using price-based and incentive programs. The objective of the work is to analyse the performance of the load shedding technique using dynamic pricing algorithm. The system was designed using multi-agent system (MAS) for a DC microgrid capable of real-time monitoring and controlling of power using price-based demand-response program. As a proof of concept, the system was implemented using intelligent physical agents, Java Agent Development Framework (JADE), and agent simulation platform (REPAST) with two residential houses (non-critical loads) and one hospital (critical load). The architecture has been implemented using embedded devices, relays, and sensors to control the operations of load shedding and energy trading in residential areas that have no access to electricity. The measured results show that the system can shed the load with the latency of less than 600 ms, and energy cost saving with an individual houses by 80% of the total cost with 2USD per day. The outcome of the studies demonstrates the effectiveness of the proposed multi-agent approach for real-time operation of a microgrid and the implementation of demand-response program.

Optimal operation of the coastal energy hub considering seawater desalination and compressed air energy storage system

An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed coastal energy hub includes generation units (wind turbine, photovoltaic cell, and combined cooling, heat and power), energy storage systems (ice storage conditioner, thermal energy storage system, and solar-powered compressed air energy storage), and seawater desalination with reverse osmosis technology as a flexible load. The purpose of the optimization is to minimize the operational and environmental costs subjected to numerous technical constraints. The suggested multi-objective problem considers the stochastic behavior of renewable generations as well as electrical, thermal, cooling, and freshwater demands under various scenarios. This paper uses an augmented epsilon constraint method for solving the proposed multi objective problem and implements a fuzzy based decision maker for choosing the suitable optimal solution amid Pareto front solutions. The proposed model implements the time of use program as a price-based demand response program. Implementation of the proposed framework on the typical coastal energy hub shows that using the solar-powered compressed air energy storage together with ice storage conditioner reduce the total operation cost (cost of purchasing electricity + cost of purchasing natural gas + cost of emissions) by 2.33% compared to the base case.