Optimizing microgrid performance a multi-objective strategy for integrated energy management with hybrid sources and demand response

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

This paper is treated with optimum energy management in a DC/AC Microgrid (MG) containing hybrid power sources to supply the load within cost minimization. In this hybrid electrical networks, energy sources are exploited as DC and AC manner, in which the Independent System Operator (ISO) should provide a practical coordination between them in order to procure the demand load optimally. This paper presents a framework that all available resources are formulated mathematically in hybrid microgrid with full constraints along with Demand Response (DR) programs implementation. The network under consideration can operate both in grid-tied and autonomous modes to manage power exchanging. Uncertainty and intermittent of Photovoltaic (PV) with Maximum Power Point Tracker (MPPT) equipped, Wind Turbine (WT), Energy Storage Systems (ESS) and DR programs are also considered to achieve the optimal control and operation. The ESSs are capable to connect both DC and AC links and the State of Charge (SOC) is maintained within permissible range. The proposed DG control framework and operation scheduling has facilitated the energy management of renewables using dynamic programing approach. A 24-hour time horizon simulation and discussion through three scenarios verified on a IEEE 33 bus distribution network, is done to represent the effectiveness of proposed energy management strategy to keep the whole hybrid grid stable.

Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid

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.

In this manuscript, a hybrid method for optimum energy management of micro grid (MG) connected system is proposed. The MG connected sources are Photovoltaic (PV) array, Wind Turbine (WT), Fuel Cell (FC), Micro Turbine (MT), Diesel Generator (DG), battery storage (BESS). The proposed technique is the consolidation of Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and Firefly Algorithm (FA). The proposed method is utilized to maintain the power flow (PF) amid the energy sources and grid. Initially, the predicted load requirement based on the ANN technique predicts the load requirement and ABC optimizes the MG configuration. Finally, the FA technique helps to reduce the FC including operation with maintenance cost. The proposed method is executed in MATLAB/Simulink site and the performance is analyzed with different existing methods like online management, ABC, ABC-ABC, and ABC-Gravitational Search (GS). Here, the maximal power generation of photovoltaic is 5.9 kW, wind turbine is 5.9 kW, micro turbine is 4 kW, FC is 4 kW, DG is 6 kW, and battery is 0.5 kW. The total cost of ABC, ABC-ABC, ABC-GS, and the proposed technique is about 4.1$/h, 3.9$/h, 3.3$/h, and 2.7$/h. Optimum micro grid combination using proposed method, overall cost comparison using different methods, statistical evaluation of MG management, cost accuracy percentage (CAP) of proposed and existing techniques, efficiency under various number of trails are also analyzed in this work. The comparison results demonstrate that the proposed technique has minimal cost-efficient based on its load requirement.

In Japan, there are many remote islands whose power systems mainly consist of internal combustion generators, and thus, the cost of energy in these areas is expensive. Therefore, renewable energy systems (RESs) such as photovoltaic (PV) and wind generator (WG) have been introduced into the power system in these areas. However, it is hard to determine the output power of these resources. Hence, battery energy storage system (BESS) is introduced. Furthermore, demand response (DR) programs which enable power supplier to control load demand, are attracting attention. DR programs are divided into time-based and incentive-based programs. In this research, real-time pricing (RTP) of time-based and interruptible load (IL) of incentive-based programs are considered as energy management method. RTP is the method that power supplier urges customer to increase or to reduce the consumption by changing electricity price corresponding to the power supply-demand situation, while IL is the customer who consented to reduce or to interrupt the power supply at emergency state of power supply-demand. This research presents the optimal sizing method of PV-WG-BESS system in 100% RES and also indicates the effectiveness of RTP and IL.

A mixed integer linear programming model for the energy management problem of microgrids

Designing an optimal Demand Response (DR) program necessitates having information on the utility of users. However, that is not easily accessible since the users may not be willing to report their personal information. In this paper, we propose an incentive-based DR mechanism for Commercial and Industrial (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I) customers, leveraging contract theory to address the challenges of incomplete information and ensure the participation of customers in the DR program. Designing a DR mechanism includes specifying an incentive reward, penalty, and the corresponding demand reduction as functions of the customer’s utility information. These functions are obtained through an optimization problem that maximizes the grid operator’s profit, ensures truthful reporting of personal information and participation of the customers in the DR program. After formulating the optimal mechanism, the main technical challenge is reformulating the obtained non-convex and computationally inefficient optimization problem to a tractable and convex one. The extensive numerical evaluations demonstrate the effectiveness of our proposed framework in comparison to existing benchmark schemes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Imbalances between supply and demand, particularly during the hot summer months, can result in issues such as power outages. This problem is detrimental for both grid operators and customers alike. DR programs have been implemented in numerous countries to address these challenges. C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I customers, due to the schedulable nature of their loads, make for ideal targets for these programs. However, the main obstacle to implementing such programs is customer participation. Incentive rewards offered in these programs for load reduction are fixed for all customers, discouraging those with low rewards from participating and imposing high costs on grid operators for those with high rewards. On the other side, designing unique rewards and penalties for each customer necessitates information about their preferences, which grid operators cannot access. In this paper, we propose an incentive mechanism based on the contract between the grid operator and C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I customers that ensures customers truthfully report their preferences while simultaneously maximizing grid operator profit and guaranteeing the participation of all customers. The proposed DR program can be implemented for large non-residential customers in order to reduce the peak load of the grid.

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

Optimal integration of demand response programs and electric vehicles in coordinated energy management of industrial virtual power plants

In this paper, a multi-objective optimization model is proposed to calculate best possible size of energy storage system (ESS). The proposed model is solved utilizing weighted sum method. Positive effects of demand response program (DRP) are considered in the proposed paper. By utilizing the weighted sum method, many various solutions are obtained. Then to select the best possible solution, fuzzy satisfying approach is employed. The proposed multi-objective model includes two conflicting objective functions: 1) the first objective function is minimization of microgrid investment cost as well as operation cost; 2) the second objective function is minimization of loss of load expectation (LOLE). Microgrid includes some local units inside itself which may have some unknown outages and also due to variable and unstable output of renewable units, utilization of ESS is essential to improve stability of microgrid. Impact of DRP implementation is evaluated on microgrid related costs and the results are compared to validate the proposed technique. In order to simulate and model the proposed stochastic ESS optimal sizing problem in a microgrid, a mixed-integer program (MIP) is utilized.

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

Charging performance of Plug-in Hybrid Electric Vehicles (PHEVs) under various charging approaches brings new challenges for the distribution grid, such as feeder overloading and increment of loss. In this paper, the impact of charging PHEVs on the load profile is evaluated considering Demand Response (DR) programs in order to minimize the impact of charging PHEVs on load profile. In the proposed DR strategy, all consumers can determine and control their loads. Also, the proposed DR strategy can satisfy the function of reducing peak with regard to different PHEV penetration, while preserving consumer comfort levels. For performing DR programs, time of use and real time pricing models are used to evaluate the impacts of charging of PHEVs on distribution system operations. A 33-node test feeder has been studied to assess different penetration schemes of PHEVs including 11.3%, 35% and 45% PHEV penetration in residential loads. Moreover, uncertainties associated with PHEV charging performances are handled with Monte Carlo simulation. Results validate that the amount of peak loss, and Peak to Average Ratio (PAR) increase owing to PHEVs penetration growth. Also, the results illustrate that scheduling for the operation of PHEVs with DR program reduces the distribution losses and PAR.

In a re-structured environment, demand response (DR) programs are presented in a new style. The demand response program consists of demand-side management methods which answers to electricity price variations. With advent of electricity markets, the demand-side management programs was introduced into two categories: (1) energy efficiency program, (2) demand response program. This paper studies engagement of participants load in an electricity market having a grid with large and small loads interested to participate in the market, energy storage systems, micro-grids, and distributed generations. In the proposed scheme, it is supposed to have an aggregator which sends demand-side preferences including load curtailment, load shifting, onsite generation, and energy storage systems along with proposed value and prices and all respective system constraints to the independent system operator (ISO). In fact, load aggregators submit aggregated DR offers to the ISO in order to optimize final decisions on aggregators’ DR contributions in wholesale market. The main goal of this paper is to solve the operation problem considering demand-side, system, and pollution and reliability constraints. Compared to other methods, the results indicate that the explicit modeling of customer DR would provide ISOs with more flexible options for scheduling the available energy resources in day-ahead energy markets.

Power outages and bill savings: A choice experiment on residential demand response acceptability in Delhi