scholarly journals Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

2019 ◽  
Vol 9 (4) ◽  
pp. 624 ◽  
Author(s):  
Tao Rui ◽  
Guoli Li ◽  
Qunjing Wang ◽  
Cungang Hu ◽  
Weixiang Shen ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Stackelberg Game ◽  
Optimization Method ◽  
Economic Benefits ◽  
Power Grids ◽  
Mixed Integer ◽  
Hierarchical Optimization ◽  
Load Demand ◽  
Control Framework ◽  
Two Stages

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

scholarly journals Exit lane allocation with the lane-based signal optimization method at isolated intersections

2020 ◽  
Vol 47 (12) ◽  
pp. 1345-1358
Author(s):  
Qinrui Tang ◽  
Alexander Sohr
Keyword(s):  
Optimization Problems ◽  
Signal Sequence ◽  
Optimization Method ◽  
Signalized Intersections ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Auxiliary Variables ◽  
Signal Optimization ◽  
Pavement Markings ◽  
Lane Based

In signal optimization problems, incompatible movements are usually in either of two states: predecessor or successor. However, if the exit lane is well allocated, the incompatible movements merging at the same destination arm can exist in parallel. The corresponding longer green duration is expected to increase the capacity of intersections. This paper aims to solve the exit lane allocation problem with the lane-based method by applying the three states among incompatible movements at conventional signalized intersections. After introducing auxiliary variables, the problem is formulated as a mixed integer programming and can be solved using a standard branch-and-cut algorithm. In addition to the exit lane allocation results, this proposed method can also determine the cycle length, green duration, start of green, and signal sequence. The results show that the proposed method can obtain a higher capacity than that without the exit lane allocation. The pavement markings are further suggested for safety.

Product Optimization Incorporating Discrete Design Variables Based on Decomposition of Performance Characteristics

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Masataka Yoshimura ◽  
Yu Yoshimura ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Mathematical Programming ◽  
Optimization Problems ◽  
System Optimization ◽  
Optimization Method ◽  
Hierarchical Optimization ◽  
Standard Material ◽  
Design Variables ◽  
Hierarchical Levels ◽  
Discrete Design Variables ◽  
Product Optimization

This paper proposes a system optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics from original characteristics. The method is constructed to take advantage of hierarchical optimization procedures, enabling the incorporation of discrete design variables. The proposed method can be applied to machine product designs that include discrete design variables such as material types, machining methods, standard material forms, and specifications. The optimizations begin at the lowest levels of the hierarchical optimization structure and proceed to the higher levels. Discrete design variables are efficiently selected and optimized in the form of small suboptimization problems at the lowest hierarchical levels, and optimum solutions for the entire problem are ultimately obtained using conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems that include several types of discrete design variables are constructed, and applied examples are provided to demonstrate their effectiveness.

scholarly journals Multi-objective hydropower purchase optimization method for inter-provincial power grids under time-sharing electricity price

2018 ◽  
Vol 246 ◽  
pp. 01043
Author(s):  
Peng Lu ◽  
Mo Li ◽  
Xiang Zhang ◽  
Yibo Zou ◽  
Shengmao Shu
Keyword(s):  
Optimization Method ◽  
Assessment Method ◽  
Economic Benefits ◽  
Electricity Price ◽  
Power Grids ◽  
Risk Allocation ◽  
Time Sharing ◽  
Customer Requirements ◽  
Electric Grid ◽  
Multi Objective

The existing electric grid purchase ratio research focuses on the methods of various types of combinations of purchasing and selling business of electricity, and the methods of reasonable risk allocation between different businesses, First, it lacks the consideration of the sensitivity of customer requirements to electricity price changes . Second, it lacks the consideration of optimization of the revenue and risk of hydropower purchase in the provincial electric grid. Therefore, this paper establishes the multi-objective time-of-use power price model based on customer requirements for time-of-use power price response.It introduces a VaR-based risk assessment method. Also, it proposes a multi-objective optimization model that maximizes the expected revenue on electricity purchase and minimizes the risk of purchasing electricity. The electricity purchase ratio scheme and the electrictiy purchase risk scheme are jointly optimized to obtain the electric grid inter-provincial electrictiy purchase risk decision and the optimal electricity purchase ratio. The results show that the grid company will obtain greater economic benefits while avoiding risks as much as possible after using the power purchase optimization method of this paper.

scholarly journals SOLVING THE NONLINEAR DISCRETE TRANSPORTATION PROBLEM BY MINLP OPTIMIZATION

Transport ◽  
2013 ◽  
Vol 29 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Uroš Klanšek
Keyword(s):  
Transportation Problem ◽  
Optimization Problems ◽  
Relaxation Method ◽  
Optimization Method ◽  
Critical Issue ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Cut Method ◽  
Optimum Solution ◽  
Selection Of

The Nonlinear Discrete Transportation Problem (NDTP) belongs to the class of the optimization problems that are generally difficult to solve. The selection of a suitable optimization method by which a specific NDTP can be appropriately solved is frequently a critical issue in obtaining valuable results. The aim of this paper is to present the suitability of five different Mixed-Integer Nonlinear Programming (MINLP) methods, specifically for the exact optimum solution of the NDTP. The evaluated MINLP methods include the extended cutting plane method, the branch and reduce method, the augmented penalty/outer-approximation/equality-relaxation method, the branch and cut method, and the simple branch and bound method. The MINLP methods were tested on a set of NDTPs from the literature. The gained solutions were compared and a correlative evaluation of the considered MINLP methods is shown to demonstrate their suitability for solving the NDTPs.

scholarly journals Robust operation of microgrid energy system under uncertainties and demand response program

2020 ◽  
Vol 17 (2) ◽  
pp. 1005
Author(s):  
Sahar Seyyedeh Barhagh ◽  
Amin Mohammadpour Shotorbani ◽  
Behnam Mohammadi-Ivatloo ◽  
Kazem Zare ◽  
Ali Farzamnia
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Optimization Problems ◽  
Energy Systems ◽  
Stochastic Scheduling ◽  
Energy System ◽  
Optimization Method ◽  
Operating Conditions ◽  
Mixed Integer ◽  
Demand Response Program

<span>Microgrid energy systems are one of suitable solutions to the available problems in power systems such as energy losses, and resiliency issues. Local generation by these energy systems can reduce the role of the upstream network, which is a challenge in risky conditions. Also, uncertain behavior of electricity consumers and generating units can make the optimization problems sophisticated. So, uncertainty modeling seems to be necessary. In this paper, in order to model the uncertainty of generation of photovoltaic systems, a scenario-based model is used, while the robust optimization method is used to study the uncertainty of load. Moreover, the stochastic scheduling is performed to model the uncertain nature of renewable generation units. Time-of–use rates of demand response program (DRP) is also utilized to improve the system economic performance in different operating conditions. Studied problem is modeled using a mixed-integer linear programming (MILP). The general algebraic modeling system (GAMS) package is used to solve the proposed problem. A sample microgrid is studied and the results with DRP and without DRP are compared. It is shown that same robustness is achieved with a lower increase in the operation cost using DRP.</span>

scholarly journals Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

2019 ◽  
Vol 11 (10) ◽  
pp. 219 ◽  
Author(s):  
Mark Kipngetich Kiptoo ◽  
Oludamilare Bode Adewuyi ◽  
Mohammed Elsayed Lotfy ◽  
Theophilus Amara ◽  
Keifa Vamba Konneh ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Economic Benefits ◽  
Mixed Integer ◽  
Optimal Size ◽  
Renewable Energy Resources ◽  
Power Prediction ◽  
Short Term ◽  
Load Demand ◽  
Battery Energy ◽  
The Right

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Product Optimization Incorporating Discrete Design Variables Based on Decomposition of Performance Characteristics

2006 ◽  
Author(s):  
Masataka Yoshimura ◽  
Yu Yoshimura ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Optimization Problems ◽  
Optimization Method ◽  
Design Solution ◽  
Hierarchical Optimization ◽  
Design Variables ◽  
Design Alternatives ◽  
Hierarchical Levels ◽  
Discrete Design Variables ◽  
Product Optimization ◽  
Alternative Designs

In order to obtain superior design solutions, the largest possible number of design alternatives, often expressed as discrete design variables, should first of all be considered, and the best design solution should then be selected from this wide set of alternative designs. Also, product designs should be initiated from the earliest possible stages, such as the conceptual and fundamental design stages, when discrete rather than continuous design variables have primacy. Although the use of discrete design variables is fundamentally important, this has implications in terms of computational demands and the accuracy of the optimized solution. This paper proposes an optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics. The optimizations are started at the lowest levels of the hierarchical optimization structure, and proceed to the higher levels. The discrete design variables are efficiently selected and optimized as smaller sub-optimization problems at the lowest hierarchical levels, while the optimum solutions for the entire problem are obtained by conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems having several types of discrete design variables are constructed, and some applied examples demonstrate their effectiveness.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

scholarly journals A Multi-Objective Approach toward Optimal Design of Sustainable Integrated Biodiesel/Diesel Supply Chain Based on First- and Second-Generation Feedstock with Solid Waste Use

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2261
Author(s):  
Evgeniy Ganev ◽  
Boyan Ivanov ◽  
Natasha Vaklieva-Bancheva ◽  
Elisaveta Kirilova ◽  
Yunzile Dzhelil
Keyword(s):  
Supply Chain ◽  
Optimal Design ◽  
Solid Waste ◽  
Second Generation ◽  
Agricultural Land ◽  
Optimization Problems ◽  
Biodiesel Production ◽  
Mixed Integer ◽  
Economic Criterion ◽  
Multi Objective

This study proposes a multi-objective approach for the optimal design of a sustainable Integrated Biodiesel/Diesel Supply Chain (IBDSC) based on first- (sunflower and rapeseed) and second-generation (waste cooking oil and animal fat) feedstocks with solid waste use. It includes mixed-integer linear programming (MILP) models of the economic, environmental and social impact of IBDSC, and respective criteria defined in terms of costs. The purpose is to obtain the optimal number, sizes and locations of bio-refineries and solid waste plants; the areas and amounts of feedstocks needed for biodiesel production; and the transportation mode. The approach is applied on a real case study in which the territory of Bulgaria with its 27 districts is considered. Optimization problems are formulated for a 5-year period using either environmental or economic criteria and the remainder are defined as constraints. The obtained results show that in the case of the economic criterion, 14% of the agricultural land should be used for sunflower and 2% for rapeseed cultivation, while for the environmental case, 12% should be used for rapeseed and 3% for sunflower. In this case, the price of biodiesel is 14% higher, and the generated pollutants are 6.6% lower. The optimal transport for both cases is rail.

Sign in / Sign up

Export Citation Format

Share Document