Multi-objective Optimal Allocation Research Articles

BFO-based firefly algorithm for multi-objective optimal allocation of generation by integrating renewable energy sources

BFO-based firefly algorithm for multi-objective optimal allocation of generation by integrating renewable energy sources

Multi-objective optimal allocation of regional comprehensive energy considering electricity trading

In order to promote the coordination of various forms of energy resources such as electric energy, heat energy, and cold energy, and to achieve complementary and efficient use of different energy sources, a method for optimizing the allocation of the capacity of a regional comprehensive energy system that takes into account the trading of electric and thermal energy is proposed. The energy interaction costs of the system and the grid heat network are included in the total cost. The optimization goal is a multi-objective function that comprehensively considers system economics, environmental protection, reliability, and interactive power fluctuations. Based on the energy hub, the capacity of each energy supply equipment in the area is optimized. Time-sharing electricity price and typical daily system economic operation are solved by genetic algorithm. Finally, an example of a park is used to verify its effectiveness. The results show that the proposed method can effectively reduce the system cost and reasonably avoid the limitations caused by the single decision factors in the system planning stage, thus providing a reference for the actual system planning and design.

BFO-based firefly algorithm for multi-objective optimal allocation of generation by integrating renewable energy sources

Among the rapid evolution of modernisation of alternative energy, the electric power system can be made out of a few Renewable Energy Resources (RES). This paper presents a modern and proficient technique for clearing up the ELD issue. To resolve this issue we have amalgamated two meta-heuristic optimisation algorithms, e.g., the Bacterial Foraging Optimisation (BFO) algorithm and the Firefly Optimisation Algorithm (FA) by incorporating both the renewable energies, such as solar and wind power. The quality of the proposed methodology is tried and approved on the standard IEEE 3, 6 and the 10-unit systems by solving some cases as the fuel cost minimisation, whole generation cost minimisation, emission minimisation, and at the same time the system transmission loss. The attained results are contrasted and the MOPSO and the hybrid BOA algorithms. The results show that the proposed methodology gives an accurate solution for some category of objective functions.

Multi-objective optimal allocation of sediment resources under multiple uncertainties

Abstract A model suitable for optimal allocation of sediment resources is utilized to cope with multiple uncertainties in the system and contradictions between allocation units. This approach incorporates inexact two-stage stochastic programming, probabilistic-possibilistic programming and fuzzy random intervals (ITPP-FRI) in an integrated framework. The multiple uncertainties of various parameters in this study are represented by the interval numbers with fuzziness and randomness. The model introduced the concept of possibility and necessity measures to address multiple uncertainties in the system, which are suitable for optimistic and pessimistic decision-makers, respectively. The model makes a tradeoff between multiple objectives including sediment siltation of each allocation unit, and economic benefits of the system. As demonstrated in the Weishan irrigation area, a number of alternative cases can be achieved at different probability levels and under varied sediment diversion scenarios. The result shows that the sediment allocation for the unit of desilting basins was significantly reduced by at least 4.3% compared with the current situation, and the units of transporting sediments to the fields and using them for producing building materials were increased by more than 15% and 5%, respectively. The sediment is allocated to the units in need and the channel siltation is reduced. According to the allocation results, a new pattern of regulating water delivery is put forward for irrigation area in this paper to meet the requirement of using limited water to transport sediment to each unit.

Multi-Objective Optimal Allocation of Water Resources Based on the NSGA-2 Algorithm While Considering Intergenerational Equity: A Case Study of the Middle and Upper Reaches of Huaihe River Basin, China.

With the rapid development of society and the economy, the demand for water resources is increasing. This, combined with the increasing competition for water resources between current and future generations, hinders the sustainable development of society. To alleviate prominent water resources problems, achieve sustainable utilization of water resources and the sustainable development of society and economy, a multi-objective optimal water resources allocation model is proposed, in which different water sources and different water departments are considered to achieve the maximum social and economic benefits of the study area on the premise of water resources sustainability. To meet the needs of future generations, the discount value is introduced to measure intergenerational equity. A case study from seven cities in the upper and middle reaches of the Huaihe River Basin is given to verify the practicality and viability of the model. The non-dominated sorting Genetic Algorithms-2(NSGA-2) was used to find optimal water resources allocation schemes in 2020 and 2050 under the condition of a hydrological drought year (inflow guarantee rate p = 75%). Compared with previous models, the intergenerational equity model considers the sustainability of water resources, has higher social and economic benefits, and ensures the fair distribution of water resources among generations. According to the results, under balanced weight, the water shortage ratio of the seven cities will decrease from 5.24% in 2050 to 1.58% in 2020, and the economic benefit will increase from 79.46(1010CNY) to 168.3(1010CNY), respectively. In addition, the discount value of economic benefit in 2050 is 80.23(1010CNY), which is still higher than that in 2020. This shows that the water resource allocation scheme can eliminate the disparity between supply and demand for water resources and achieve intergenerational equity. Therefore, the intergenerational equity model can alleviate the contradiction of water resources and realize intergenerational equity.

Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models

This work introduces multi-objective water cycle algorithm (MOWCA) to find the accurate location and size of distributed energy resource (DERs) considering different load models for two seasons (winter, and summer). The impact of uncertainties produced from load and renewable energy resource (RES) such as wind turbine (WT) and photovoltaic (PV) on the performance of the radial distribution system (RDS) are covered as this is closer to the real operation condition. The point estimate method (PEM) is applied for modeling the RES uncertainties. An optimization technique is implemented to find the multi-objective optimal allocation of RESs in RDSs considering uncertainty effect. The main objectives of the work are to maximize the technical, economic and environmental benefits by minimizing different objective functions such as the dissipated power, the voltage deviation, DG cost and total emissions. The proposed multi-objective model is solved by using multi-objective water cycle algorithm (MOWCA), considering the Pareto criterion with nonlinear sorting based on fuzzy mechanism. The proposed algorithm is carried out on different IEEE power systems with various cases.

Optimal allocation of regional water resources based on multi-objective dynamic equilibrium strategy

Raising water demand, insufficient freshwater resources and uneven precipitation are the main causes of water conflicts in the river basin. The main challenges in water resources allocation are the following: first, the uncertainty of water supply caused by climate change; second, the dynamic allocation of water resources is not considered; third, there is a lack of equitable water allocation, which may lead to intensified conflicts among the water user sectors. Based on the above challenges, a multi-objective dynamic programming optimal water resources allocation model under uncertainties is established in this paper, which could be a resolution for water disputes as it addresses simultaneously economic, social and environmental benefits. The proposed model considers maximizing the minimum satisfaction degree of each water sector, maximizing the utilization efficiency of water resources in the basin, and maximizing economic benefits. Satisfaction degree function is introduced to optimize water allocation equality in water use sectors (agricultural, domestic, and industrial sectors), and economic efficiency loss acceptance is integrated into the model constraints to control the economic loss corresponding to variations in water availability. In addition, a case study demonstrated the practicality and rationality of the proposed model, and the model results proposed herein show significantly help for a single river basin. Most importantly, the results show that model with dynamic constraint ensures a greater overall economic return for the entire basin, and a larger variation in water resources of sub-area leads to a larger maximum economic efficiency loss. Moreover, the proposed model herein is flexible and can be used in other developing countries/regions only by modifying the parameters according to the actual situation.

Research on the Evaluation Model of Multi-objective Optimal Allocation of Water Resources in Guizhou Province

Aiming at the characteristics of multiple water sources and multiple users in Guizhou’s urban water system, this paper establishes a multi-objective allocation model of urban water resources. The paper uses multi-objective programming to solve the model, and analyzes the impact of different target weights on the water resources allocation results of Guizhou Province in the fuzzy multi-objective decision model. The results show that the optimization model and fuzzy decision are reasonable and feasible.

The multi-objective optimal allocation method of static var compensator based on transient voltage security control

Based on the detailed consideration of the dynamic characteristics of Static Var Compensator (SVC), the mathematical method for installing location and optimal capacity of SVC considering transient voltage security is proposed. Firstly, the trajectory sensitivity index is used to determine the key fault set and SVC candidate location. Then, transient voltage security constraint of the bus voltage is restored to 0.75pu within 1s and the bus voltage is restored to 0.9pu within 3s after remove fault, Finally, considering the transient voltage recovery characteristics and dynamic characteristics of SVC, the minimum of bus voltage deviation after fault removed and minimum of installtion capacity of SVC as objective function for hybrid optimal allocation model. The bus voltage and capacity of SVC are introduced as variables into the Crisscross Optimization Algorithm (CSO) for solving. The effectiveness of the proposed SVC installation location and capacity optimization method is verified by the time domain simulation of the IEEE39-bus system.

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

The acceptance rate of Electric Vehicles (EVs) in the transport industry has increased substantially due to the augmented interest towards sustainable transportation initiatives. However, their impact in terms of increased power demand on the electric power market can increase real power losses, decrease voltage profile, and consequently decrease voltage stability margins. It is necessary to install Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) at optimal locations to decrease the EV load effect in the Radial Distribution System (RDS). This paper addresses a multi-objective optimization technique to obtain simultaneous EVCS & DG placement and sizing. The problem is formulated to optimize real power losses, Average Voltage Deviation Index (AVDI), and Voltage Stability Index (VSI) of the electrical distribution system. Simulation studies were performed on the standard IEEE 33-bus and 69-bus test systems. Harries Hawk Optimization (HHO) and Teaching-Learning Based Optimization (TLBO) algorithms were selected to minimize the system objectives. The simulation outcomes reveal that the proposed approach improved system performance in all aspects. Among HHO and TLBO, HHO is reasonably successful in accomplishing the desired goals.

Multi-objective optimal allocation of water resources based on ‘three red lines’ in Qinzhou, China

Abstract Water shortages and pollution emerge because of anthropogenic demands. Since 2011, ‘China's Most Stringent Water Resources Management’ (CMSWRM) has been comprehensively enacted in the country. This paper presents the characteristics of the ‘three red lines’ (TRL) and a multi-objective optimal allocation model based on the TRL constraint, considering the benefits for society, the economy, and the environment. This model had been applied to the reasonable allocation of water supply and demand in Qinzhou for the planning years of 2020 and 2030. Two water resource allocation scenarios for these years were configured by setting different chemical oxygen demand (COD) concentrations for wastewater discharge in the municipal, secondary, tertiary, and agricultural sectors. The gamultiobj function based on the NSGA-II algorithm was used to solve the model in MATLAB. The results indicate that if COD concentrations in each sector are not reduced, then restrictions on domestic water sources will be necessary, both in 2020 and 2030. The two water resource allocation scenarios in 2020 and 2030 can provide a reference for decision-makers in Qinzhou to implement CMSWRM.

A multi-objective optimal allocation of treated wastewater in urban areas using leader-follower game

This study proposes a new method for optimal allocation of Treated Wastewater (TW), in which different stakeholders, their social position in decision-making, and priority of objectives were attended using the leader-follower game theory. The suggested methodology was applied in a case study in the eastern part of Tehran province in Iran, where the Water and Sewage Department is considered the leader and four TW dependent districts are the followers in the game model. The leader appropriates a certain TW quantity to the system, and the followers compete for the allocated resources in the face of various physical and sociopolitical constraints. The Nash-Harsanyi production function was applied to model the non-cooperative relationships among the followers (i.e., their competition for limited resources) and find a compromise solution. Considering different limitations, such as the location and quantity of the TW allocation, 1569 different allocation scenarios were considered in four districts. Then, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based multi-objective optimization model was developed to optimize the main objectives of the leader, including i) minimizing the TW transfer costs, and ii) minimizing the supplied demand deficiency in all districts. A multi-criteria decision-making model was utilized to find the best solution on the achieved Pareto-front space. Nine different weighting scenarios were adopted to assess the model sensitivity to the importance of the selected criteria. Results point to the sensitivity of the framework to weighting scenarios, but provide effective compromise solutions to a complex system that can only partially supply water demands.

Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China

To improve the overall efficiency of the energy system, the basic structure for the energy internet of coordination and optimization of “generation-grid-load-storage” of Huangpu District, Guangzhou, China is designed, while the arrangement for the output of centralized and distributed energy module and energy storage are proposed. Taking economic benefit maximization, environmental benefit maximization and energy efficiency maximization as sub-objectives, the mathematical model of multi-objective optimal allocation and operation strategy of the energy internet is established considering supply-demand balance constraints, equipment characteristic constraints, operation mode constraints, and energy conditions constraints. The calculation results show that without considering the outsourced electricity, the balanced strategy, the economic development strategy, the environmental protection strategy, and the energy efficiency strategy are obtained by calculation, which are all superior to the traditional energy supply strategy. Moreover, considering the outsourced electricity, the proportion of outsourced electricity to total electricity is 19.8%, which is the system optimization of the energy internet under certain power demand. Compared with other strategies without outsourced electricity, the outsourced electricity strategy can have a certain emission reduction effect, but at the same time reduce the economic benefit. Furthermore, the huge difference in demand for thermal and cooling load between industrial and commercial areas results in the installed capacity of gas distributed energy stations in industrial areas being nearly twice as large as that in commercial areas. The distributed photovoltaic power generation is allocated according to the proportion of the installed roof areas of photovoltaic power generation system in residential, industrial, and commercial areas.

Multi-Objective Optimal Allocation of Wireless Bus Charging Stations Considering Costs and the Environmental Impact

In recent years, due to environmental concerns, there has been an increasing desire to develop alternative solutions to traditional energy sources. Since transportation is a significant fossil-fuel consumer, the development of electric vehicles, especially buses, has the potential to reduce fossil-fuel use and thus provide a better living environment. The aim of the current work was to develop an optimal allocation model for designing a system-wide network of wireless bus charging stations. The main advantages of wireless charging are the need for a much smaller battery and the fact that the charging process may occur under both static and dynamic (in-motion) conditions. The suggested approach consisted of a multi-objective model that selected the locations for the charging stations while (a) minimizing the costs, (b) maximizing the environmental benefit, and (c) minimizing the number of charging stations. The problem was formulated as a multi-objective non-linear optimization model with both deterministic and stochastic variations. An efficient genetic algorithm was introduced to solve the problem. A test case was used to demonstrate the model; accordingly, the decision-maker was provided with a solution set from which the best fit solution could be selected.

Multi-Objective Optimal Allocation of Urban Water Resources While Considering Conflict Resolution Based on the PSO Algorithm: A Case Study of Kunming, China

With the rapid increase of water demand in urban life, ecology and production sectors, the problem of water resources allocation has become increasingly prominent. It has hindered the sustainable development of urban areas. Based on the supply of various water sources and the water demand of different water users, a multi-objective optimal allocation model for urban water resources was proposed. The model was solved using the algorithm of particle swarm optimization (PSO). The algorithm has a fast convergence and is both simple and efficient. In this paper, the conflict over Kunming’s water resources allocation was taken as an example. The PSO algorithm was used to obtain optimized water resources allocation plans in the year 2020 and 2030, under the circumstances of a dry year (inflow guarantee rate p = 0.825) and an unusually dry year (inflow guarantee rate p = 0.885), respectively. The results showed that those allocation plans can lower the future potential water shortage rates of Kunming. At the same time, the interests of different sectors can all be satisfied. Therefore, conflicts over urban water use can be effectively alleviated.

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution System Using Teaching Learning Based Optimization

In recent times, the adoption rate of Electric Vehicles (EVs) in the transportation sector has been increased significantly across the world towards sustainability. On the other side, the increasing EV load penetration in the electric power sector can cause for the generation-demand imbalance, real power loss increment, weak voltage profile and consequently voltage stability margin decrement. It is essential to integrate EV Charging Stations (CSs) at appropriate locations to mitigate the impact of increasing EV load penetration on radial distribution systems (RDS). In this paper, the teaching-learning based optimization (TLBO) algorithm is applied to determine the optimal locations of EV-CSs considering multiple objectives, i.e., real power loss, average voltage deviation index and voltage stability index. The simulation studies are performed on standard IEEE 33-bus and 69-bus test systems. The results have highlighted the need for optimal allocation of EV-CSs for maintaining the system performance as better as possible even under increased loading conditions due to EV-CSs. Also, TLBO has shown its ability over other heuristic algorithms namely particle swarm optimization (PSO), ant lion Optimizer (ALO), flower pollination algorithm (FPA) and cuckoo search algorithm (CSA) by providing the optimal value consistently in solving the complex non-linear multi-objective optimization problem.

Fuzzy programming method for multi-objective optimal allocation of sediment resources and the cooperative bargaining: a case study in Weishan irrigation area, China.

The optimal allocation of sediment resources needs to balance three objectives including ecological, economic, and social benefits so as to realize sustainable development of sediment resources. This study aims to apply fuzzy programming and bargaining approaches to solve the problem of optimal allocation of sediment resources. Firstly, Pareto-optimal solutions of multi-objective optimization were introduced, and the multi-objective optimal allocation model of sediment resources and fuzzy programming model was constructed. Then, from the perspective of multiplayer cooperation, the optimal allocation model of sediment resources was transformed into a game model by using Nash bargaining, and Nash bargaining solution was obtained as the optimal equilibrium strategy. Finally, the influence of different disagreement utility points and bargaining weights on the results was discussed, and the results of Nash bargaining and fuzzy programming methods were compared and analyzed. Results corroborate that Nash bargaining can achieve the cooperative optimization of multiple objectives with competitive relationship and obtain satisfactory scheme. Disagreement utility points and bargaining weights have a certain impact on the optimization results. The solution of fuzzy programming is close to that of Nash bargaining, which provides different ideas for multi-objective optimization problem.

A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels

In this paper, the performance of different optimization techniques namely, multi-objective dragonfly algorithm (MODA) and multi-objective differential evolution (MODE) are presented and compared. The uncertainty effect of a wind turbine (WT) on the performance of the distribution system is taken into account. The point estimate method (PEM) is used to model the uncertainty in wind power. Optimization methods are applied to determine the multi-objective optimal allocation of distributed generation (DG) in radial distribution systems at a different load level (light, normal, heavy load level). The multi-objective function is expressed to minimize the total power loss, total operating cost, and improve the voltage stability index of the radial distribution system (RDS). Multi-objective proposed algorithms are used to generate the Pareto optimal solutions; and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution. The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.

Research on the Allocation of Flood Drainage Rights of the Sunan Canal Based on a Bi-level Multi-Objective Programming Model

To reduce flood disasters and optimize of the comprehensive benefit of the water basin, the allocation of regional flood drainage rights is of great significance. Using the “top-down” allocation mode, we consider the influence of the social, economic, and ecological environments, flood drainage demand and efficiency, and other factors on the allocation of flood drainage rights. A bi-level multi-objective programming model from the perspective of fairness and efficiency is established for the allocation. The Sunan Canal is taken as a typical case study. The model is solved by the multi-objective optimal allocation method and the master–slave hierarchical interactive iteration algorithm. After three iterations of the initial solution, the allocation of flood drainage rights in six flood control regions finally reach an effective state. The results of the model were compared with results based on historical allocation principles, showing that the bi-level multi-objective programming model, based on the principles of fairness and efficiency, is more in line with the current social and economic development of the canal. In view of the institutional background of water resources management in China and the flood drainage pressure faced by various regions, the allocation of flood drainage rights should be comprehensively considered in combination with various factors, and the market mechanism should be utilized to optimize the allocation.