Optimization Of Distribution Networks Research Articles

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

Optimal integration of DSTATCOM using improved bacterial search algorithm for distribution network optimization

Due to the non-linear nature of most of the loads, there is a greater demand for reactive power in the distribution network. Further, it wasted high energy on distribution and dropped the bus voltages. The integration of the distribution static synchronous compensator (DSTATCOM) mechanism into the power distribution sector increases load capacity, compensates reactive power, and reduces power losses while enhancing the voltage profile and stability. This research utilized the improved bacterial foraging search algorithm (IBFA) for the optimum sizing and placement of the DSTATCOM in the distribution sector to minimize power losses and improve voltage stability and profile. The power flow model is developed using the back-forward load flow technique. The outputs obtained are compared with those from the conventional bacterial foraging algorithm (BFA) and the base case scenario. The optimum size of DSTATCOM computed is 3697.3kVAr with the optimal placement at bus 19. The percentage drop in power losses obtained with the installation of DSTATCOM using an improved bacterial search algorithm (IBFA) is 49.021% as compared to the base case scenario. Similarly, the IBFA approach resulted in a 12.8% enhancement in voltage stability and a 3.32% improvement in voltage profile as compared to the base case scenario. The results obtained clearly indicate the efficiency and applicability of the applied IBFA technique to the optimum placing and sizing of DSTATCOM in the radial power distribution sector for minimizing power loss and improving the voltage profile and stability.

Real-time optimization of active distribution networks with distributed energy resources participating in frequency regulation

Dispatchable distributed energy resources (DERs) in distribution networks are envisioned to aid frequency regulation for transmission systems. In this paper, a real-time optimal dispatch framework for DERs in distribution networks is designed to offer frequency regulation services simultaneously. Different from the existing research that distribution networks track uniaxially the predetermined auxiliary-services commands of the transmission system, here we regard transmission system frequency regulation as a black box and learn the parameters of its proxy satisfaction function from the perspective of DER optimization. To solve such a special optimization problem with control performance feedback, first we employ Gaussian processes to learn the satisfaction function, and, especially, build pertinent upper confidence bounds to achieve the optimal provision of ancillary services. Next, the primal-dual gradient projection process is embedded into the Gaussian process upper confidence bound algorithm to pursue the optimal DER dispatch. Accordingly, the output powers of DERs can be controlled in real-time: in disaggregate mode, they meet the goal of the distribution network; in aggregate mode, they provide a more satisfactory tie-line power flow to the transmission system. Simulations for illustrative systems are provided to validate the approach.

Optimization of Urban Water Distribution Networks Using Deterministic and Heuristic Techniques: Comprehensive Review

AbstractWater distribution networks (WDNs) are systems of water distribution used in urban centers and industrial processes that require efficient design and operation, which may be achieved throug...

Mathematical Programming and Solution Approaches for Transportation Optimisation in Supply Network

The problem of transport is a special type of mathematical programming designed to search for the optimal distribution network, taking into account the set of suppliers and the set of recipients. This article proposes an innovative approach to solving the transportation problem and devises source codes in GNU Octave (version 3.4.3) to avoid the necessity of carrying out enormous calculations in traditional methods and to minimize transportation costs, fuel consumption, and CO2 emission. The paper presents a numerical example of a solution to the transportation problem using: the northwest corner, the least cost in the matrix, the row minimum, and Vogel’s Approximation Methods (VAM). The joint use of mathematical programming and optimization was applicable to real conditions. The transport was carried out with medium load trucks. Both suppliers and recipients of materials were located geographically within the territory of the Republic of Poland. The presented model was supported by a numerical example with interpretation and visualization of the obtained results. The implementation of the proposed solution enables the user to develop an optimal transport plan for individually defined criteria.

Utilizing Matrix Completion for Simulation and Optimization of Water Distribution Networks

Simulation of water distribution networks (WDNs) constitutes a key element for the planning and management of water supply systems. The literature presents different formulations of heads-flows equations that differ in terms of dimensionality, computational cost, and solution accuracy. Whereas this problem has been the subject of active research in the past, in the last decades a state of stagnation was reached and no new formulations were introduced. In this study, we propose a novel formulation that utilizes a matrix completion technique to construct a reduced-size nonlinear system of equations that guarantees both mass and energy conservation. The advantages of the proposed method are demonstrated in simulation and optimization settings. In the former, the method demonstrates improved scalability and accuracy as compared with other widely known formulations. In the latter, the new formulation leads to compact optimization problems that result in better performance in terms of the objective function, run time, and rate of successful optimization runs.

A Hybrid Approach for Optimization of Distribution Networks with Photovoltaic Sources

This paper presents a hybrid approach, which associates a mathematical programming technique with a metaheuristic, to optimize the electricity distribution network with penetration of photovoltaic (PV) generation. This optimization considers the distribution network reconfiguration (DNR) and the adjustment of the power factor of photovoltaic (PV) inverters in relation to the reactive power, controlling the voltage profile and minimizing network losses. The methodology uses particle swarm optimization (PSO) for DNR and an optimal power flow (OPF) to adjust the value of the PV inverters and maintenance of network operating limits. The tests were performed using the AMPL Modeling Language in the 33-node system with the addition of PV generation. The approach considers the variations in the powers generated by the PV systems and the variations in the loads over a 24 h period. The results indicate that the combination of PV inverter control and DNR can reduce the active power losses by more than 37% in a 24-hour period.

Reliability-based optimization of water distribution networks

Abstract The water distribution system serves as a basic necessity for society. Due to its large size and involvement of various components, it is one of the most expensive civil infrastructures and thus demands optimization. Much work has been done to reduce the distribution system cost. However, with only one objective, the obtained solutions may not be practical to implement. Thus, improving cost along with the efficiency of the network is the demand of the hour. The present work introduces a unique parameter-less methodology for generating Pareto fronts without involving the concept of non-dominance. The methodology incorporates the Jaya optimization model for a bi-objective problem, one being the reduction in network cost and the other improving the reliability index of the network. The efficiency of the proposed work is analyzed for three different benchmark problems. The Jaya technique is found to be very efficient and fast when compared with the other evolutionary technique applied for the same networks. The parameter-less nature of the Jaya technique smooths the process to a very large extent as no synchronization of algorithm parameters is required.

Distributed optimal-tracking control to coordinate the optimization of active distribution networks with automatic generation control

To the shortage of automatic generation control (AGC) resources in power systems with high proportions of renewable generators, this paper proposes an optimal-tracking control strategy to integrate the optimization of active distribution networks (ADNs) into AGC. Different ADNs can coordinate to absorb the power fluctuations beyond AGC adjustment ranges in a distributed way. First, we build a time-varying model to integrate the optimization of ADNs into AGC, which gives the power allocation mechanism among different ADNs and AGC units in real time. Second, we design a distributed strategy to continuously drive the operation of ADNs to the solutions of the time-varying model using the optimal-tracking sensitivities calculated based on the feedback of measurements. In this strategy, the measurements of net load powers are calculated using the integral of the area control error in AGC; moreover, the optimal-tracking sensitivities are decomposed into several items of which each one only relates to the inner information of the transmission network or an ADN, which is deduced according to the physical laws of power flow of transmission and distribution networks. Based on the decomposition, each ADN can form the optimal-tracking sensitivities only based on its inner information and some coordination information from the transmission network. Thus, the distributed optimal-tracking controller is achieved. Finally, the effectiveness of the proposed control strategy is verified by case studies: ADNs are regulated to effectively restrain the excessive frequency deviations, caused by the sharp fluctuations of renewable generator outputs, with meeting the operational constraints of ADNs (e.g. the constraints on voltages) in real time.

Economic based evaluation of DGs in capacitor allocated optimal distribution network

Economic based evaluation of DGs in capacitor allocated optimal distribution network

Multi Objective Reactive Power Optimization of Distribution Network with Distributed Generation Power Uncertainty

Distributed generation will have a lot of adverse effects on the steadiness of the power system, resulting in the power wastage of the Distribution Network (DN) and the destruction of the reactive power balance of the power grid. It is essential to establish a dynamic reactive power optimization model to ameliorate the security, steadiness and economy of the DN. Based on this, this paper first analyses the connotation and typical characteristics of Distributed Generation (DG), then studies the connotation and typical characteristics of DG, and finally gives the multi-objective reactive power optimization strategy of DG DN.

Application of Parameter-Less Rao Algorithm in Optimization of Water Distribution Networks Through Pressure-Driven Analysis

Application of Parameter-Less Rao Algorithm in Optimization of Water Distribution Networks Through Pressure-Driven Analysis

Mobile and self-powered battery energy storage system in distribution networks–Modeling, operation optimization, and comparison with stationary counterpart

Spatio-temporal and power-energy controllability of the mobile battery energy storage system (MBESS) can offer various benefits, especially in distribution networks, if modeled and employed optimally. Accordingly, this paper presents a novel and efficient model for MBESS modeling and operation optimization in distribution networks. Given the transportation sector's transition towards electrification, a self-powered electric truck is considered for conveying the whole battery system. As a result, the required energy for transportation is obtained from the main battery system. The proposed model demands only transportation time between distribution network buses without modelling the whole transportation network. Novel straightforward and efficient formulations consider transportation time and cost limitations by linear equations capable of handling real-world systems without dimensionality problems. The model is implemented on a sample system, and its results are compared with the stationary batteries. The simulation results demonstrate the proposed model's effectiveness to obtain a minimum cost operation plan accompanied by the enhanced technical performance compared to the stationary battery installations. By employing the MBESS in a typical distribution network, the total operation cost is reduced by more than 4%, while total losses and maximum substation power are reduced by, in turn, 740 kWh and 690 kVA.

A two-stage stochastic model for co-firing biomass supply chain networks

A sustainable alternative to fossil fuels is biomass. The design of cost-efficient biomass networks is necessary to compete with nonrenewable resources. The creation of integrated biomass supply chain (BSC) network design models and solution procedures can contribute to the achievement of this goal. In particular, the emerging bioenergy industry must take advantage of the economies of scale in transportation to minimize the final product’s cost. Hub-and-spoke networks have been proposed as a modeling approach to design large scale BSCs. The majority of these models are deterministic and do not consider the inherent variability in the biomass feedstock, such as physical and chemical properties that affect transportation, the effects of future climate on the biomass supply, overall cost, and production operations. Levels of ash and moisture are directly related to the quality of the feedstock, which negatively affects production of biofuels, increasing transportation and handling costs as well as the burden on the BSC’s efficiency. Varying weather affects biomass yield, which creates a fluctuation in the incoming supply into the network, creating a complex large scale Newsvendors Problem. In this paper, a stochastic hub-and-spoke network model and an efficient solution are proposed to minimize logistical costs by finding an optimal production and distribution network as well as optimal transportation while reducing computational burden by using metaheuristics, such as Simulated Annealing, when solving large NP-hard instances. The proposed solution procedure was compared to recent benchmark models, i.e. Bender’s Decomposition, yielding an overall solution difference of less than 7.71% and average reduction time difference of 91.4%. Case studies with several scenarios based on varying weather conditions were created using realistic data from the northeast region of the U.S.

Optimal electric distribution network configuration using adaptive sunflower optimization

Network reconfiguration (NR) is a powerful approach for power loss reduction in the distribution system. This paper presents a method of network reconfiguration using adaptive sunflower optimization (ASFO) to minimize power loss of the distribution system. ASFO is developed based on the original sunflower optimization (SFO) that is inspired from moving of sunflower to the sun. In ASFO, the mechanisms including pollination, survival and mortality mechanisms have been adjusted compared to the original SFO to fit with the network reconfiguration problem. The numerical results on the 14-node and 33-node systems have shown that ASFO outperforms to SFO for finding the optimal network configuration with greater success rate and better obtained solution quality. The comparison results with other previous approaches also indicate that ASFO has better performance than other methods in term of optimal network configuration. Thus, ASFO is a powerful method for the NR.

Optimal reconfiguration of balanced and unbalanced distribution systems using firefly algorithm

Abstract With increasing demand in energy, efficiency, reliability, and resiliency in the power systems, integration of distributed generation (DG) or renewable energy sources in the local distribution system is trending nowadays. The power system optimization in distribution networks plays a prominent role in planning reliable supply to the users. This paper presents a meta-heuristic approach to improve the system performance by considering: a) voltage profile improvement, b) minimize the active power loss, and c) overall operation cost as the objective functions of the distribution system reconfiguration (DSR). Modified Newton Raphson (MNR) is used for a load flow analysis of the 33 bus balanced test system and 25 bus unbalanced distribution system. To study the performance of the system objective functions, three different scenarios are considered: i) with DG integrated, ii) with DSR performed, and iii) DG placed along with DSR. These scenarios are compared with the base system configuration parameters along with convergence comparison of harmony search algorithm (HAS) and particle swarm optimization (PSO) with firefly algorithm (FFA) of the distribution systems.

A New Method for Optimization of Water Distribution Networks While Considering Accidents

Optimization of water distribution networks can effectively reduce their annual cost, which includes the average investment for each year the operational costs and depreciation costs. However, the existing optimization models rarely directly consider the basic flow of each node in case of accidents, such as pipe bursts. Therefore, it is necessary to check the flow requirements under accident conditions. In order to deal with these drawbacks, two optimization models are established considering accident conditions: a single-objective optimization model considering annual cost as an economic objective, and a multi-objective optimization model with a reliability objective defined by the surplus water head. These models are solved based on the genetic algorithm, non-dominated sorting genetic algorithm-II algorithm and Levenberg-Marquardt iterative method. Applying two cases of a single pump station and a multi pump water station water supply, it is shown that the annual cost when considering the accident conditions is higher than that without considering the accident conditions. Moreover, the annual cost obtained with the multi-objective optimization model is slightly higher than that obtained with the single-objective optimization model. The cost is higher because the former model reduces the average surplus water head, which can improve the water distribution network reliability. Therefore, the model and optimization algorithm proposed in this paper can provide a general and fast optimization tool for water distribution network optimization.

Optimization and Computing Model of Fish Resource Supply Chain Distribution Network

Aceh is a province that is rich in fishery resource potential. Fish resources become one of the leading commodities, therefore we need a supply chain optimization model. Optimization is one of the technologies widely used in supply chain network management. Fish resource supply chain becomes very important because it is caused by several factors, including the diversity of types of products from fish resources and their short expiry. The steps used in this research is literature review, determine parameters and decision variables, formulate the objective function and constraint function, modeling and model implementation. The purpose of this research is to get an optimization model in planning and managing the fish resource supply chain by using mixed integer linear programming, so that the resulting model can solve problems in the limitations of fish resources in certain areas, can distribute all consumer demand quickly. This new model can minimize transportation costs to suppliers, minimize transportation costs from suppliers to distribution centers and minimize transportation costs from distribution centers to consumers, and minimize product inventory costs to suppliers and to distribution centers.

Multi-objective robust optimization of active distribution networks considering uncertainties of photovoltaic

The conventional single objective optimization cannot meet the requirements for the optimal operation of active distribution networks (ADNs) with multiple uncertainties brought by the distributed generation (DG), diverse demand responses and flexible topologies etc. In addition, the contradictions among multiple objectives, the tradeoff between the robustness, conservative, and deterioration of performance, as well as the computation cost have challenged the solution of multi-objective optimization, in particular for the ADNs with multiple uncertainties along with complicated operation scenarios. Considering the uncertainties of distributed photovoltaic (PV), a multi-objective robust optimization strategy for ADNs is proposed in this paper to maximize the utilization of renewables and to minimize the network losses at the same time. By constructing the uncertainty set of predicted PV’s maximum outputs, the extreme scenarios of the uncertainty set are chosen to establish constraints, and reduce the dimension of calculation. Then, the multi-objective optimization algorithm can obtain the Pareto fronts under various ranges of uncertainty sets while satisfying the physical and operating constraints of the ADNs, the influence of various ranges of uncertainty set on the Pareto front is taken into consideration. The effectiveness and robustness of the proposed model are verified on a modified IEEE 33 bus system, and the scalability is tested on a 141 bus distribution network.

A Survey on Computational Intelligence Applications in Distribution Network Optimization

This paper aims to present carefully selected scientific papers that have pushed the boundaries in the application of advanced computational intelligence–based methods in power engineering, mainly in optimal power system management. Contemporary development of the Smart Grid and detailed framework for power grid digitalization enabled the real and efficient application of advanced optimization algorithms presented in this paper. Papers that are not directly related to Smart Grid management are also considered, since they solve the partial challenges of planning and development with metaheuristic procedures, and according to the authors, they are highly applicable and represent a fundamental starting point for wider application. This paper covers papers and research whose results are reproducible and can be realized in production-grade software. The emphasis of the paper is on the considerate and impartial way of providing a concise overview of the methods for solving technical challenges within the accepted Smart Grid architecture. The paper is the result of many years of research and commitment to this field and represents the foundation for present research and development.