An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

A multi-objective energy optimization in smart grid with high penetration of renewable energy sources

Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management

A genetic algorithm optimization approach for smart energy management of microgrids

WITHDRAWN: Short term optimal scheduling of a virtual power plants integrated with electrothermal hybrid energy storage system using an effective adaptive multi-objective differential evolution algorithm

The huge computational overhead is the main challenge in the application of community based optimization methods, such as multi-objective particle swarm optimization and multi-objective genetic algorithm, to deal with the multi-objective optimization involving costly simulations. This paper proposes a Kriging metamodel assisted multi-objective particle swarm optimization method to solve this kind of expensively black-box multi-objective optimization problems. On the basis of crowding distance based multi-objective particle swarm optimization algorithm, the new proposed method constructs Kriging metamodel for each expensive objective function adaptively, and then the non-dominated solutions of the metamodels are utilized to guide the update of particle population. To reduce the computational cost, the generalized expected improvements of each particle predicted by metamodels are presented to determine which particles need to perform actual function evaluations. The suggested method is tested on 12 benchmark functions and compared with the original crowding distance based multi-objective particle swarm optimization algorithm and non-dominated sorting genetic algorithm-II algorithm. The test results show that the application of Kriging metamodel improves the search ability and reduces the number of evaluations. Additionally, the new proposed method is applied to the optimal design of a cycloid gear pump and achieves desirable results.

Development of energy storage system scheduling algorithm for simultaneous self-consumption and demand response program participation in South Korea

The demand response (DR) program is an effective solution to promote the low‐carbon operation of power systems with increasing penetration of renewable energy sources (RESs). This paper proposes a low‐carbon DR program for power systems to enhance both the environmental friendliness and uncertainty resilience of the system operation. The system operator aims to minimize both the system's operation cost and carbon trading cost. To handle the uncertainty associated with stochastic RES generation power and load consumption power, a data‐driven method named the two‐sided distributionally robust chance‐constrained (TS‐DRCC) approach is adopted to enhance the system's uncertainty resilience. A ladder‐type carbon trading scheme is utilized to calculate the carbon emission cost of the system. Comprehensive analyses of case studies have been conducted to validate that the proposed strategy can effectively reduce the total carbon emissions and total operation costs with good uncertainty resilience performance. The proposed low‐carbon DR program is verified to achieve 63.64% more carbon emission reduction compared with the conventional DR program. Besides this, the proposed low‐carbon DR program can also achieve 4.39% carbon‐intensive generation power reduction and 5.52% RES power consumption compared with the conventional DR program.

Multi-objective optimization of vapor absorption refrigeration system for the minimization of annual operating cost and exergy destruction

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

In this article, we present a multi-objective discrete particle swarm optimizer (DPSO) for learning dynamic Bayesian network (DBN) structures. The proposed method introduces a hierarchical structure consisting of DPSOs and a multi-objective genetic algorithm (MOGA). Groups of DPSOs find effective DBN sub-network structures and a group of MOGAs find the whole of the DBN network structure. Through numerical simulations, the proposed method can find more effective DBN structures, and can obtain them faster than the conventional method.

In order to manage peak-grid events, utilities run incentive-based demand response (DR) programs in which they offer an incentive to assets who promise to curtail power consumption, and impose penalties if they fail to do so. We develop a stochastic model for the curtailment capability of these assets, and use it to derive analytical expressions for the optimal participation (i.e., promised curtailment) and profitability from the DR asset perspective. We also investigate the effects of risk-aversion and curtailment uncertainty on both promised curtailment and profit. We use the stochastic model to evaluate the benefits of forming asset aggregations for participation in DR programs, and develop a numerical test to estimate asset complementarity. We illustrate our results using load data from commercial office buildings.

Energy cost optimization through load shifting in a photovoltaic energy-sharing household community

A load control method for small data centers participating in demand response programs

Purpose– The purpose of this paper is to propose a multi-objective particle swarm optimization (MOPSO) algorithm based design optimization of Brushless DC (BLDC) motor with a view to mitigate cogging torque and enhance the efficiency.Design/methodology/approach– The suitability of MOPSO algorithm is tested on a 120 W BLDC motor considering magnet axial length, stator slot opening and air gap length as the design variables. It avails the use of MagNet 7.5.1, a Finite Element Analysis tool, to account for the geometry and the non-linearity of material for assuaging an improved design framework and operates through the boundaries of generalized regression neural network (GRNN) to advocate the optimum design. The results of MOPSO are compared with Multi-Objective Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II based formulations for claiming its place in real world applications.Findings– A MOPSO design optimization procedure has been enlivened to escalate the performance of the BLDC motor. The optimality in design has been out reached through minimizing the cogging torque, maximizing the average torque and reducing the total losses to claim an increase in the efficiency. The results have been fortified in well-distributed Pareto-optimal planes to arrive at trade-off solutions between different objectives.Research limitations/implications– The rhetoric theory of multi objective formulations has been reinforced to provide a decisive solution with regard to the choice of the design obtained from Pareto-optimal planes.Practical implications– The incorporation of a larger number of design variables together with an orientation to thermal and vibration analysis will still go a long way in bringing on board new dimensions to the fold of optimality in the design of BLDC motors.Originality/value– The proposal offers a new perspective to the design of BLDC motor in the sense it be-hives the facility of a swarm based approach to optimize the parameters in order that it serves to improve its performance. The results of a 120 W motor in terms of lowering the losses, minimizing the cogging torque and maximizing the average torque emphasize the benefits of the GRNN based multi-objective formulation and establish its viability for use in practical applications.

This paper presents an investigation into the development of a multi-objective optimal chemotherapy control model to reduce the number of cancer cells after a number of fixed treatment cycles with minimum side effects. A phase specific drug scheduling method using a close-loop control method with multi-objective techniques is proposed in this paper. Genetic Algorithm (GA) and particle swarm optimisation algorithm (PSO) are used to optimise the control solution for trading-off between the cell killing and toxic side effects. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused into the patient's body and multi-objective GA (MOGA) and multi-objective PSO (MOPSO) are used to find suitable parameters of the controller. The proposed algorithm is implemented, tested and verified through a set of experiments. Performances of the proposed methods demonstrated that both the MOGA and MOPSO approach can offer very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals. It is also noted that the MOGA based method offers better performance as compared to MOPSO and can reduce the number of proliferating and quiescent cells up to 72.2% and 60.4% respectively. Future research needs to evaluate the proposed scheduling with clinical data and experiments.