Time-based Demand Response Programs Research Articles

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.

Optimal management with demand response program for a multi-generation energy system

• Methodology for the simultaneous optimization of load shifting and MES management. • Optimal dispatch strategy of real-world load profiles over one year. • Primary energy consumption reduction of about 1% compared to baseline scenario. • Operational cost reduction of about 8% compared to baseline scenario. The optimal management of a multi-generation energy system is one of the challenges that an ever-growing energy demand requires to deal with. To tackle this urgent issue, this paper presents a methodology to identify the optimal dispatching strategy for a multi-generation energy system. The so-called time-of-use rate is one of the main time-based demand response programs which allows shifting the critical loads from a time interval to another (e.g., shifting electricity use to lower-priced hours of a day when demand is lower). Thus, the time-of-use rate is adopted in this paper in order to add flexibility to the management of the multi-generation energy system and thus optimize the interaction between energy production and user demand. In this paper, the goal is the minimization of primary energy consumption or operating costs. Whatever the considered objective function, the goal can be achieved by simultaneously acting on two levels, i.e., optimization of the demand response program and identification of the most favorable management strategy of the multi-generation energy system. A mixed-integer linear programming algorithm is employed to identify the optimal strategy. The case study considers an entire year of operation with a time step of one hour, by means of a real-world load profile. The proposed methodology allows both saving primary energy (more than 1%) and reducing operating costs (more than 8%). The proposed methodology demonstrates that the implementation of a demand response program within the optimal strategy for energy dispatch allows both saving primary energy and reducing operating costs with respect to the baseline scenario (i.e., no load shifting). The reduction of both primary energy consumption and operational costs is higher in the scenario with higher load shifting (in this paper, 30% of the daily electrical energy peak).

Demand response programs in power systems with energy storage system-coordinated wind energy sources: A security-constrained problem

This study seeks to provide a secure operation for wind-integrated transmission networks by proposing a computationally efficient daily DC security-constrained optimal power flow model. The proposed model determines the active power initial set-points of the energy resources. Moreover, energy storage systems and demand response programs are contributed to the system to yield a flexible model. In this regard, a practical formulation for the energy storage system is included in the proposed model to cope with the undesired output power fluctuations of wind energy sources. To assure the scalability of the proposed model, a contingency filtering scheme is developed to filter the unnecessary contingencies from the optimization process for reducing the computational burden and solution time. Furthermore, the side-effects of wind energy fluctuations and the component outages are techno-economically explored and measured. The measures are the operational cost of the transmission network, the number of critical contingencies, peak-to-valley ratio, and load shedding. The numerical simulations on the New England 39-bus and IEEE 118-bus testbeds confirm the proper performance and good scalability of the proposed model. A priority list of the time-based demand response programs is also presented in each case of the study. The added value of the demand response programs on the security and economic performance of the proposed model is also tailored.

Supervised learning‐based demand response simulator with incorporation of real time pricing and peak time rebate

Demand response (DR) program empowers the dynamic prices to actively optimize the consumption. This optimized consumption plays a vital role in resolving the complex operation and reliability issues in the electricity market. The human behavior aspect of consumers explained by several models that have been reported in the literature. These models depend on the classical utility factor. The effect of price on the consumer's decision in the field of energy efficiency and reduction of consumption based on behavioral characteristics are two important aspects of DR programs. In absence of such characteristics, results become non-viable. In this paper, the footprint of two time-based DR programs is explored on the peak reduction namely; Real-Time Pricing (RTP) and Peak Time Rebate (PTR). Artificial Neural Network (ANN) based topologies for two DR programs are proposed. The proposed topologies employ variation in demand and price, subsequently for simulating an online DR simulator. Demand before and after the RTP and PTR was calculated and compared with four ANN-based DR topologies namely; Radial Basis Function Neural Network-Demand Response (RBFN-DR), Feedforward Backprop-Demand Response (FFBP-DR), Layer Recurrent-Demand Response (LR-DR), and Generalized Regression-Demand Response (GR-DR). The proposed models are tested on three test cases. The first case tested on hourly data of New England ISO of Connecticut on August 18, 2014, the second case tested on hourly data of the same system on August 18, 2020, and the third case tested on hourly residential data of test smart grid. By assessing the results from all three test cases, depicted that RBFN-DR proved its efficacy by giving better results for both price-based programs namely; RTP and PTR.

Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs

In recent years, rapidly increasing electric power demand and organizational concerns regarding social and environmental issues have resulted in significant attention being given to microgrids. However, sustainable microgrids that simultaneously address economic benefits and environmental and social issues have not been broadly explored by researches. This study addresses the sustainable microgrid design problem by leveraging blockchain technology to provide the real time-based demand response programs. Three sustainable objectives (economy, environment, and society) are formulated by a multi-objective mixed integer-linear programming model. A robust fuzzy multi-objective optimization approach is proposed to determine the optimal number, location, and capacity of renewable distributed generation units as well as the equilibrium supply and dynamic pricing decisions under uncertain demand, capacity, and economic, environmental, and social parameters. The proposed model and solution approach are then applied to a case study in Vietnam. We find that blockchain technology-based sustainable microgrid can result in a 1.68% and 2.61% increase of profitability and consumer satisfaction, respectively, and a 0.97% reduction of environmental impacts.

Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

Dynamic Multi-Carrier Microgrid Deployment Under Uncertainty

An accurate assessment of microgrid economic benefits is a challenging task due to various uncertain data involved in the assessment. This paper scrutinizes the economic viability of multi-carrier microgrid deployments under various uncertainties, incorporating smart grid technologies, namely dispatchable and nondispatchable resources, energy storage devices, and demand response programs. The optimal type, size, and commissioning year of the available distributed energy resources along with economic utilization of selected resources are assessed to be implemented in the proposed multi-carrier microgrid. In this study, the stochastic multi-objective function comprises the net present value costs associated with the multi-carrier microgrid investment and installation, operation, maintenance, emission, incentive and penalty payments, and contracted power between multi-carrier microgrid owner and local utility company. Furthermore, an economic model for time-based demand response programs along with voluntary and mandatory demand response programs is developed innovatively. It correlates the local energy price of elastic loads for electrical and thermal carriers with day-ahead hourly pricing, energy import amounts, and on-site generations. The proposed dynamic planning problem under uncertainties of electrical and thermal loads, electricity price, and solar irradiation is solved by using a two-stage stochastic programming method that combines the genetic algorithm of MATLAB and the mixed-integer nonlinear programming model of GAMS software. Numerical simulation analyses demonstrate the effectiveness of the proposed multi-carrier microgrid expansion planning problem from the economic, environmental, and technical points of view.

Optimal scheduling of the RIES considering time-based demand response programs with energy price

With the revolution of the traditional economic and social pattern based on centralized fossil energy consumption, the regional integrated energy system (RIES) has gained rapid development in recent years for achieving higher energy efficiency. This paper expands the Demand Response (DR) concept to the RIES and presents an optimal operation model of RIES considering the DR mechanism on the energy price. In this paper, a comprehensive DR strategy combining energy price and different loads is firstly developed to exploit the demand flexibility of RIES in the DR model established. Based on the DR modeling of the RIES, an operation optimization model with environmental, economic benefits and energy supply reliability as objective functions has been firstly established in detail. Secondly, a complete scheduling scheme is built based on the energy consumption characteristics and system operation characteristic of the integrated energy system. The model presented could reduce the cost of system without causing a significant amount of environmental pollution, and improve the energy efficiency of the RIES efficiently. Besides, the scheduling strategy proposed could also provide support for the operation strategy of integrated energy system under the energy development. Hopefully, this paper will provide reference for future research and engineering projects on DR programs in the RIES.

Integration of smart grid technologies in stochastic multi-objective unit commitment: An economic emission analysis

This paper proposes a stochastic multi-objective unit commitment (SMOUC) problem incorporating smart grid technologies (SGTs), namely, plug-in electric vehicles (PEVs), demand response programs (DRPs), compressed air energy storage (CAES) units, and renewable distributed generations (DGs). An economic emission analysis of the proposed SMOUC problem with the SGTs is carried out to minimize the total expected operation cost and emission using a new mixed-integer linear programming (MILP) method. A two-stage stochastic programming method is used for dealing with the uncertain nature of power generation from the renewable DGs. Lexicographic optimization in combination with hybrid augmented-weighted ε-constraint method are employed to obtain Pareto optimal solutions of the SMOUC problem, and a fuzzy decision making is applied to select the most preferred non-dominated solution. Besides, mathematical modeling of responsive loads can help the independent system operator (ISO) to use a conservative and reliable model to have lower error in load curve characteristic estimation, such as variation in peak load. In this regard, this paper also contributes to the existing body of knowledge by developing linear and nonlinear economic models of price responsive loads for time-based DRPs (TBDRPs), as well as voluntary and mandatory incentive-based DRPs (IBDRPs) based on the customer’s behavior (CB) concept and price ratio (PR) parameter. Also, new mathematical indices are proposed to choose the most conservative and reliable economic model of price responsive loads. Moreover, different widely used DRPs are analyzed and prioritized using the strategy success index (SSI) from the ISO viewpoint to determine the most effective DRP which has more coordination with the SGTs. The proposed MILP-based SMOUC problem with integrated SGTs, is applied to IEEE 10-unit test system and is implemented in General Algebraic Modeling System (GAMS) environment. Simulation analyses demonstrate the effectiveness of integrating SGTs into the proposed SMOUC problem from the economic, environmental, and technical points of view.

First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources

Integration of different energy resources at the customers’ premises in recent years, advances in Information and Communication Technologies (ICTs), and Advanced Metering Infrastructure (AMI) systems are becoming attractive tools for developing new real time demand response at the supplier side and the management of energy resources at the customers’ side. The management programs can be classified as; smart grid management from the supplier side and intelligent Energy, ‘‘i-Energy”, from the consumer energy management side. There are two types of programs; (i) time based program like real time pricing and (ii) incentive based demand response. A combination of these two programs is proposed in this paper with the merged program to be real time with incentive demand response. The time based demand response programs can be improved by using smart metering infrastructure and different resources. The incentive demand comes from the feasibility of providing new concept known as ‘i-energy’ at the customers’ sides. To achieve this, Smart Meters (SMs) and different resources at the customers’ premises using this concept are applied. By integrating different resources at the customers’ premises, using the i-energy concept, can change the limitation given in the time based program. The first developed program at the supplier side depends on purchasing MW from the customers who participate in the program. The second contribution is the ‘‘i-Energy” management technique at the customers’ side that is based on congestion and potential games through strategy of load control using different resources. Revenue for different participants in the program from the commercial and industrial sectors, at different levels of reduction and different usage of different resources, is discussed.

The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid

In this work, an accurate energy consumption model of household appliances based on Set of Sequential Uninterruptible Energy Phases (SSUEP) is applied to day-ahead energy management framework of a residential microgrid in order to effectively activate time-based demand response programs. The homes in the microgrid include the essential and/or shiftable household appliances accurately modeled by the SSUEP. These homes are also equipped with the photovoltaic systems, battery energy storages and electric vehicles. The residential microgrid is assumed to be connected to a smart grid such that bi-directional exchange of electric power would be possible. Being aware of the amount of power demand for the appliances and the day-ahead prices of the energy, the consumer provides the required energy from the photovoltaic systems, battery energy storages and electric vehicles (by Vehicle-to-Home and Vehicle-to-Grid capabilities). Moreover, using the flexibility of the shiftable loads, the consumer can be involved in the demand response strategies to reduce the costs. This flexibility is a result of delaying or anticipating the start time and the inter-phase delay modeled by the SSUEP. Lastly, the effects of the accurate SSUEP model on the day-ahead planning of the residential microgrid will be investigated by various scenarios.

Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty

Networked microgrids (NMGs) are beneficial and economical for both microgrids’ owners and consumers as this structure could potentially play a significant role in energy efficiency, power system reliability and sustainability. Renewable energy sources (RESs) and sharp fluctuations in load consumption impose new challenges in solving operational problems in smart distribution grids. As a result, deterministic methods are not able to provide a precise analysis of microgrids operation and planning. Therefore, stochastic algorithms are used as powerful tools in ensuring reliable solutions especially in operation problems. In this paper, daily optimal scheduling problem of NMGs considering intermittent behavior in generation and load is investigated in a proposed energy management system (EMS). Two demand response programs (DRPs) based on time of use (TOU) and real time pricing (RTP) are integrated into the optimal scheduling model and the developed model is solved using a metaheuristic algorithm under uncertainties of RESs and loads. The numerical simulations show the effectiveness of the proposed model through comparison with solution from stochastic optimization.

4456960 Method and device for detecting tool abnormality in machine tools : Hideyuki Wakai, Hirakata, Japan assigned to Kabushiki Kaisha Komatsu Seisakusho

In this work, an accurate energy consumption model of household appliances based on Set of Sequential Uninterruptible Energy Phases (SSUEP) is applied to day-ahead energy management framework of a residential microgrid in order to effectively activate time-based demand response programs. The homes in the microgrid include the essential and/or shiftable household appliances accurately modeled by the SSUEP. These homes are also equipped with the photovoltaic systems, battery energy storages and electric vehicles. The residential microgrid is assumed to be connected to a smart grid such that bi-directional exchange of electric power would be possible. Being aware of the amount of power demand for the appliances and the day-ahead prices of the energy, the consumer provides the required energy from the photovoltaic systems, battery energy storages and electric vehicles (by Vehicle-to-Home and Vehicle-to-Grid capabilities). Moreover, using the flexibility of the shiftable loads, the consumer can be involved in the demand response strategies to reduce the costs. This flexibility is a result of delaying or anticipating the start time and the inter-phase delay modeled by the SSUEP. Lastly, the effects of the accurate SSUEP model on the day-ahead planning of the residential microgrid will be investigated by various scenarios.