IEEE 33-bus Distribution System Research Articles

A Distributed Online Learning Approach for Energy Management With Communication Noises

An online distributed neurodynamic optimization method for energy management in smart grids is proposed to minimize the operational cost of smart grids, considering various constraints, such as output bounds, state of charge of battery storage systems, network congestion and voltage limits. The proposed distributed neurodynamic optimization method adopts a consensus protocol using the signs of the information differences between neighbors for information exchanging and a recurrent neutral network for optimization without dual multipliers. It has the following merits. First, the calculation and information exchange of dual multipliers are avoided to mitigate the computational complexity and reduce communication bandwidth. Second, the proposed optimization method is robust against communication noises from various components. Third, the uncertainties of renewable energy and loads are addressed by the online optimization approach, which takes advantage of the historical data. The convergence, optimality, consensus and robustness of the proposed distributed neurodynamic optimization method are rigorously proved in theory and verified in case studies on the modified IEEE 33-bus and IEEE 123-bus distribution systems.

IOT-based social-economic management of distribution system with the high penetration of renewable energy sources

• A new internet of things frame work is developed for smart power grids with high data measure accuracy and low control latency. • A stochastic framework based on the point estimate approach (PEA) is developed which can handle the uncertainty effects through some limited concentration points. • Social-economic analysic of distribution systems with renewable sources. • Deploying IoT to monitor the system and minimize the cost. • Introducing the modified CSA for solving the problem in the stochastic environment. This paper investigates the social and economic aspects of the distribution system in the presence of different types of renewable energy sources. It suggests an internet-of-thing (IoT) based framework to monitor and measure the distribution system data with higher accuracy and controllability with very low latency. The proposed multi-objective framework considers the cost objective function as the economic index and the total customer interruption costs as the social index. A min-max fuzzy multi-objective framework is designed to optimize the objective functions using the modified clonal selection algorithm (CSA). In order to enhance the social-economic aspects of the distribution system, optimal reconfiguration is analyzed as a useful tool for the operator. A stochastic framework based on the point estimate approach (PEA) is developed which can handle the uncertainty effects through some limited concentration points. The appropriate performance of the proposed model is assessed on the IEEE 69-bus distribution system.

Bi-Level Multi-Objective Planning Model of Solar PV-Battery Storage-Based DERs in Smart Grid Distribution System

This paper presents a novel bi-level multi-objective model for planning solar PV-battery storage (PV-BS) based DERs in Smart grid distribution system (SGDS). The planning of Solar PV and battery storage (BS) in the distribution system presents severe complexities for the system planner due to their inherent attributes. The detailed analysis considering all three planning aspects, namely technical, economics, and environmental, is presented, assisting in the efficient and reliable planning operation. The four test cases are formulated based upon the life cycle cost, unserved energy, penetration level, and social welfare. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">level-1</i> proceeds with evaluating the DERs sizing along with penetration level and serves as an input to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">level-2</i> for computing the optimal placement for DERs. The proposed model is implemented on the IEEE-33 bus distribution system and solved using Butterfly-PSO (BF-PSO) algorithm. The detailed parametric and sensitivity analysis presented may aid the system planners in terms of planning preview. The simulation results validate the effectiveness of the proposed work by showing significant improvement in bus voltage profiles (up to 4%) and reduction in total power losses (up to 45%). The highest penetration level is observed for Case 2 (70%), which also corresponds to the highest LCC (k <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 147094) and thus results in the lowest emission cost (k <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 4920).

Cost-Effective Coordinated Voltage Control in Active Distribution Networks With Photovoltaics and Mobile Energy Storage Systems

High penetration of intermittent renewable energy resources (RES) will impose great challenges on the conventional voltage control in distribution networks. This paper proposes a two-stage cost-effective coordinated voltage control strategy to mitigate fast voltage violations while minimizing the total voltage regulation cost. In the first stage, the optimal power flow (OPF) is performed to determine the day-ahead dispatch results of on-load tap changers (OLTC) and capacitor banks (CBs). The hourly charging schedule of mobile energy storage systems (MESS) is also obtained through a spatial-temporal decision model embedded in the optimization problem. In the second stage, the real-time voltage control strategy is implemented to regulate the power output of photovoltaics (PVs) and MESS with one minute resolution. A novel fine-tuning control method is proposed to adjust the power output, which can further reduce voltage regulation cost within a certain voltage dead band. To reserve more fast-response power resources for PVs and MESS, the voltage regulation cost curves are designed as a piecewise linear function with different per unit cost in each range. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the enhanced control effect in mitigating voltage violations and saving costs.

Stacked Revenues Maximization of Distributed Battery Storage Units Via Emerging Flexibility Markets

Power system flexibility is seen as a key for addressing the management challenges in the future smart grids. Distributed Energy Resources (DERs) can provide the necessary flexibility to: a) maintain a stable frequency and a secure energy supply in an overall system perspective, and b) maintain bus voltages and secure transfer capacities in their local networks. In this context, Flexibility Service Providers (FSPs) facilitate the management of the transmission and the distribution network, while at the same time they optimally exploit their DERs. This work proposes a bilevel model for an FSP owning distributed Battery Storage Units and participating in: i) wholesale Energy, Reserve and Balancing Markets, and ii) a novel distribution-level Flexibility Market. The developed model is applied to the IEEE 33-bus radial distribution system and the results demonstrate that it achieves superlinear gains. Finally, a sensitivity analysis is conducted to study the impact of several externalities on the FSP's decisions.

A Software Defined Networking Architecture for DDoS-Attack in the Storage of Multimicrogrids

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

Optimal location and sizing of distributed generation to minimize losses using whale optimization algorithm

The conventional power plants often bring in power quality concerns for instance high power losses and poor voltage profile to the network which are caused by the locations of power plants that are placed a distance away from loads. With proper planning and systematic allocation, the introduction of distributed generation (DG) into the network will enhance the performance and condition of the power system. This paper utilizes the optimization approach named whale optimization algorithm (WOA) in the search of the most ideal location and size of DG while ensuring the reduction of power losses and the minimization of the voltage deviation. WOA implementation is done in the IEEE 33-bus radial distribution system (RDS) utilizing MATPOWER and MATLAB software for no DG, one DG and two DGs installation. The outcome obtained from using WOA was compared to other well-known optimization methods and WOA has shown its competency after comparison; the optimal location of WOA with other methods showing almost the same result. The best result presented was the system with two DGs installed due to the losses of the system was recorded to be the least compared to one DG or no DG installation.

Multi-Objective Optimization for Optimal Allocation and Coordination of Wind and Solar DGs, BESSs and Capacitors in Presence of Demand Response

The renewable energy sources (RES) based distributed generations (DGs) have been proven to be of great technical and economic benefits if optimally allocated in distribution networks. Their proper deployment is usually made in conjunction with demand response (DR) programs and renewables curtailment to match the demand load with the available generated power. Battery energy storage systems (BESSs) and capacitor banks (CBs) are two other tools that can compensate for the shortcomings of the RES. BESSs could complement the renewables generation intermittency and improve the reliability of the system while CBs could indemnify the limited reactive power support of the RES and improve the power quality of the system. Thus, the simultaneous integration of RES-based DGs, DR, BESSs, CBs, and curtailment could have great benefits if optimally planned and coordinated. In this paper, a multi-objective optimization planning model is formulated to determine the optimal locations and capacities of different RES-based DGs, BESSs, and CBs in presence of DR and renewables curtailment to maximize the economic index and the average voltage stability factor, and to minimize the average power losses in distribution networks. The modelling of RES-generated power is considered. The proposed model is implemented and tested on standard IEEE 33-bus radial distribution system, the model is formulated and solved in GAMS environment. Different renewable configurations and test cases are investigated. The obtained results validate the effectiveness of the simultaneous integration of the used tools in optimizing the techno-economic benefits.

Performance Enhancement of Distribution Systems via Distribution Network Reconfiguration and Distributed Generator Allocation Considering Uncertain Environment

The emergence of dispersed generation, smart grids, and deregulated electricity markets has increased the focus on enhancing the performance of distribution systems. This paper proposes a method to reduce the energy loss and improve the reliability of distribution systems by performing distribution network reconfiguration (DNR) and distributed generator (DG) allocation. In this study, the intermittent nature of renewable-based DGs and the load profile are considered using a probabilistic method. The study investigates different annual plans based on the seasonal power profiles of DGs and the load to minimize the combined cost function of annual energy loss and annual energy not served. The proposed method is implemented using the firefly algorithm (FA), which is one of the meta-heuristic optimization algorithms. Several case studies are investigated using the IEEE 33-bus distribution system to highlight the effectiveness of the method.

Distribution System Operation With Renewables and Energy Storage: A Linear Programming Based Multistage Robust Feasibility Approach

Distribution systems are operated with an increasing level of uncertainty. Energy storage is playing an important role in shaving the peak load and mitigating uncertainty. This paper proposes a multistage robust optimization model for distribution system operation with energy storage under uncertainty. Unlike the conventional robust optimization paradigm which minimizes the worst-case cost, the proposed formulation optimizes the cost in the nominal scenario. In analogy to dynamic programming, we define dynamic robust feasible regions in a recursive manner. In each period, the dynamic robust feasible region is shown to be polyhedral, and a linear programming based projection algorithm is developed to compute such regions offline. In the online stage, the method is executed following a rolling horizon manner: renewable output is observed at the beginning of each period, and the cost of remaining periods in the forecast scenario is to be minimized subject to operation constraints and dynamic robust feasible regions, giving rise to a linear program. In this way, the dispatch strategy ensures multistage operation security regardless of future realizations of renewable power. In numeric tests on a modified IEEE 33-bus distribution system, the dynamic robust feasible regions are visualized and analyzed, and the proposed method is compared with two prevailing robust optimization methods, verifying its advantages in terms of optimality and robustness.

Maximum Hosting Capacity Assessment of Distribution Systems With Multitype DERs Using Analytical OPF Method

Renewable energy is being increasingly integrated into distribution systems worldwide in response to technological, economic, and environmental challenges. The assessment of hosting capacity allows us to determine the maximum installation capacity of distributed energy resources (DERs) in a distribution system within its operational limits to obtain more benefits. In this study, a new multistage algorithm is developed based on an analytical approach and optimal power flow (OPF) for the assessment of DERs’ hosting capacity (DERHC) with single and multiple multi-type DERs. In the first stage, the optimal locations of DERs are determined analytically, and the second stage involves the calculation of optimal DERs sizes for the assessment of the maximum locational and total DERHC. This method provides mathematical and global optimum certainty considering the constraints to maintain the reliability and protection of the system. Moreover, the proposed method is tested using a standard IEEE 33-bus distribution system, and different scenarios are created based on the number and type of DERs to achieve the best-case results of DERHC. The obtained results are compared with those of the conventional OPF iterative method that are encouraging and validate the accuracy and robustness of the proposed methodology.

An Evolutionary Game Theory-Based Optimal Scheduling Strategy for Multiagent Distribution Network Operation Considering Voltage Management

Distribution system operators (DSOs) have difficulty in scheduling distributed energy resources owing to the increasing power demand and penetration of renewable energy. The goal of this study is to determine the charging/discharging of PV energy-integrated energy storage system (PV-ESS), EV charging price, and demand response (DR) incentive values considering voltage management. To achieve the optimal energy operation for a distribution network, this study proposes an evolutionary game theory (EGT)-based new scheduling strategy, considering voltage management for a multi-agent system (MAS). The EGT, which is a decision-making strategy, is used by agents to cooperate and derive the best scheduling with their own behavior pattern functions to minimize the system operating cost. Photovoltaic-energy storage systems, electric vehicles charging power, and loads can perform charging/discharging scheduling, electric vehicle charging planning, and demand response participation, respectively. Under DSO supervision, a reward that stabilizes the voltage profile of the power distribution system is also implemented during the cooperation process. The proposed energy scheduling strategy combines an EGT-based decision-making with particle swarm optimization (PSO) to solve the optimization problem and determine the payoff function through self-evolutionary improvement. The effectiveness of the EGT-PSO has been analyzed for an IEEE 33-bus distribution system, and the results demonstrate that the proposed scheduling strategy not only achieves the most economical decision among agents but also manages the voltage profile.

Optimal Reconfiguration of Power Distribution Network Using Hybrid Firefly and Particle Swarm Optimization Algorithm

Electrical energy has become the most essential requirement for working of today’s modern world. Power distribution networks (PDN) are required for providing power from substations to consumers but are subjected to power loss and voltage drop problems. These problems greatly affect the operational cost and voltage stability level of a PDN. Network reconfiguration (NR) is a cost effective approach to optimize PDN for reduction of power loss and improvement of voltage profile (VP). This paper presents an effective meta-heuristic, population-based algorithm for finding optimal configuration of a PDN. In particular Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm is used. The HFPSO algorithm has enhanced exploration and exploitation strategies, and fast convergence rate. MATLAB software is used to implement the algorithm and IEEE 33-bus radial distribution system (RDS) is considered for NR. The results obtained show that active power loss is reduced by 46.35% from original value and minimum voltage is improved to 0.9572p.u. The comparison of obtained results with literature show that HFPSO algorithm has efficiently reduced active power loss and improved VP of the network.

Power Quality and Performance Assessment of Grid-connected Photovoltaic Distributed Generation with Compliance to Stipulated Grid Integration Requirements

Recently, the demand for electrical energy has increased more than energy production due to the growing population and industrialization. Therefore, the distributed generators integration (DGs) into the distribution system has been widely adopted. This work examines the effect of photovoltaic-based distributed generator (PV-DG) integration on power quality effect of a radial distribution system. Firstly, the capacity and optimum placement of the PV-DG units in the distribution network are determined by employing the particle swarm optimization (PSO) algorithm. Then, the impact of PV-DG integration on voltage harmonic distortion is analyzed by performing harmonic load flow analysis. Also, the P-V curve method is used to evaluate the effects of higher PV-DG penetration levels on loading margin and voltage magnitude. The simulation results show that as the PV-DG units’ penetration level increases, a greater level of harmonic distortion is injected, implying that the PV-DG units should only be integrated up to the network’s maximum capacity. Therefore, high harmonic distortion is produced when the PV-DG units are penetrated beyond this maximum penetration level, which has a negative impact on the system’s performance. The total voltage harmonic distortion is 4.17 % and 4.24 % at PCC1 and PCC2 at the highest penetration level, allowing the acceptable harmonic distortion limit. Also, grid-connected PV-DG units improve loading margin and voltage magnitude, according to the P-V curve results. The standard IEEE-33 bus distribution system is modelled in ETAP software and is used as a test system for this study.

Power Quality and Performance Assessment of Grid-connected Photovoltaic Distributed Generation with Compliance to Stipulated Grid Integration Requirements

Recently, the demand for electrical energy has increased more than energy production due to the growing population and industrialization. Therefore, the distributed generators integration (DGs) into the distribution system has been widely adopted. This work examines the effect of photovoltaic-based distributed generator (PV-DG) integration on power quality effect of a radial distribution system. Firstly, the capacity and optimum placement of the PV-DG units in the distribution network are determined by employing the particle swarm optimization (PSO) algorithm. Then, the impact of PV-DG integration on voltage harmonic distortion is analyzed by performing harmonic load flow analysis. Also, the P-V curve method is used to evaluate the effects of higher PV-DG penetration levels on loading margin and voltage magnitude. The simulation results show that as the PV-DG units’ penetration level increases, a greater level of harmonic distortion is injected, implying that the PV-DG units should only be integrated up to the network’s maximum capacity. Therefore, high harmonic distortion is produced when the PV-DG units are penetrated beyond this maximum penetration level, which has a negative impact on the system’s performance. The total voltage harmonic distortion is 4.17 % and 4.24 % at PCC1 and PCC2 at the highest penetration level, allowing the acceptable harmonic distortion limit. Also, grid-connected PV-DG units improve loading margin and voltage magnitude, according to the P-V curve results. The standard IEEE-33 bus distribution system is modelled in ETAP software and is used as a test system for this study.

Optimal Local Volt/Var Control for Photovoltaic Inverters in Active Distribution Networks

The penetration of photovoltaics (PVs) has been increasing in active distribution networks (ADN), which leads to severe voltage violation problems. PV inverters can provide fast and flexible reactive power support and are now allowed to participate in the voltage regulation process. This paper proposes a real-time combined central and local Volt/Var control (VVC) strategy to mitigate voltage violation problems while minimizing the network power loss. Based on the historical PV and load data, the load flow and optimal power flow are performed in the centralized controller (CC) to obtain multiple voltage and optimal power settings for each PV system. The local controller (LC) then generates voltage control curves with these optimal scatters. To improve the voltage control effect, a novel 3-Dimension voltage control curve is proposed considering both the measurements of node voltage and PV generation. Moreover, a data-driven deep convolution neural network is designed and trained to generate optimal local voltage control curves without prior knowledge of specific curve functions. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness in voltage control compared with the optimal Q(V) and Q(P) method.

Hierarchical Voltage Control Strategy in Distribution Networks Considering Customized Charging Navigation of Electric Vehicles

This paper proposes a three-layer hierarchical voltage control strategy for distribution networks considering the customized charging navigation of electric vehicles (EVs). In the first layer, optimal power flow (OPF) is performed to determine the day-ahead dispatch of on-load tap changer (OLTC) and capacitor banks (CBs). The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second layer, a customized charging navigation strategy is proposed to conduct the charging behaviors of EVs based on their own preferences. The novel preference modes are designed for different types of EV users to include not only the charge and time cost, but also the willingness to participate in voltage regulation service (VRS). The navigation problem is formulated as a mixed-integer linear programming (MILP), which is then solved by CPLEX solvers embedded with Dijkstra algorithm. In the third layer, charging stations measure local voltage and regulate the charging power of EVs to mitigate voltage violation. The charging selection and power allocation process are performed dynamically considering the mutual effect between the second and third layers. The economic compensation mechanism is also designed for both EV users and charging stations. The proposed approach is tested on the IEEE 33-bus distribution system coupled with a 24-bus transportation system, and simulation results verify the effectiveness both in charging navigation and mitigating voltage violation.

Enhancement of an Iraqi Radial Distribution System Performance Using Multi-Object Particle Swarm Optimization

This paper proposes a Multi Object Particle Swarm Optimization (MOPSO) to find the optimal location and capacity of Distributed Generation and D-STATCOM device. The objective function has adapted with a multi -objectives function to improve voltage profile, the voltage stability and reduce the total power loss of the Radial Distribution System (RDS). Basically, the voltage stability index (VSI) has been used to pre-determine the optimal location of DG and D-STATCOM. Then, a MOPSO applies to achieve the suitable size of DG and D-STATCOM units with different load models. The proposed method is compared with other existing methods and it was obtained losses reductions and enhancement voltage stability and average voltage when was tested on IEEE 33-bus and real Iraqi 65-bus radial distribution system through simulation using MATLAB. Furthermore, different cases were considered for using the (MOPSO) algorithm at different load factors.

A data-driven accurate battery model to use in probabilistic analyses of power systems

Analytical battery models depend on a set of complex nonlinear equations that make them impractical to use in probabilistic analyses (e.g., reliability evaluation) of power systems. Machine-learning algorithms have the potential to reduce or even avoid the computational complexities of incorporating actual battery characteristics in probabilistic analyses. In this paper, a neural network (NN)-based approach is proposed to develop a battery model that captures the non-linear interactions between charging/discharging power and battery state of charge (SoC). In the proposed approach, an NN with a rectified linear unit activation function is trained using historical data generated from an experimentally validated battery model. Another NN with linear activation function is trained to capture the relationship between charging/discharging power limits and SoC. Weights and biases of the trained networks in conjunction with mixed integer linear programming are used to develop an accurate and computationally attractive battery model. Also, a mathematical model is formulated to accommodate the proposed battery model in power system reliability evaluation. Moreover, a genetic algorithm-based approach is used to determine optimal locations for batteries considering the developed model to enhance reliability of power systems. The proposed approach is demonstrated on a modified version of the IEEE 33-bus distribution system. Monte Carlo simulation is performed to calculate reliability indices. The results show that the proposed battery model is effective to incorporate actual battery characteristics in probabilistic analyses (evaluating reliability and finding optimal battery locations) of power systems.

Distribution network congestion management considering time sequence of peer-to-peer energy trading

In the deregulated energy market environment, small-scale peer-to-peer (P2P) energy trading can increase the distributed photovoltaic power generation consumption and promote local energy balance. However, it also increases the possibility of security constraints violations during the operation of the utility grid. To solve physical network congestion caused by distributed P2P energy trading, we propose a method that considers the time sequence of P2P energy trading based on a continuous double auction (CDA) mechanism. In addition, to make full use of the users’ flexibility to solve the network congestion, a two-tier market (P2P energy market and ancillary service market) coordination mechanism is proposed. The congestion management model includes two parts. One is congestion responsibility determination and congestion cost allocation in the P2P energy market, and the other is flexible services purchasing in the ancillary service market for minimizing congestion costs. The cost and benefit constraints are specially added in the congestion management model to reduce the economic loss of the grid and users. The proposed method is tested on 11-bus and IEEE 33-bus radial distribution systems to illustrate the effectiveness and scalability of this method. The simulation results show that the proposed mechanism and model effectively perform congestion management considering distributed P2P energy trading, and the results are profitable to users participating in both markets.