Improve conservation voltage regulation effects by integrating more distributed renewable generations

Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand

Energy management plays a critical role to accomplish a reliable and efficient operation of a Microgrid (MG). As the power from renewable energy sources and load demand is uncertain energy management problem (EMP) of a MG becomes complex. This paper studies the EMP for a grid connected MG consisting of dispatchable distributed generators (DDGs), battery, and renewable generation such as wind power, respectively. In order to handle the uncertainties ascending from wind power and load demand, information gap decision theory (IGDT) is adopted in this paper. An illustrative case study is presented to demonstrate the applicability of the proposed method. Results show that least robustness is achieved on modeling uncertainty in both wind power and load demand in comparison to the modeling of uncertainty in either parameter.

Introduction. Constant changes in electrical system loads lead to increased power losses and voltage drops, requiring effective strategies to improve grid performance amid changing power demands. Problem. Many studies assume constant loads when determining optimal locations for distributed generation (DG) units, when in reality, loads change throughout the day. These changes affect network performance and require efficient solutions that adapt to changes in loads demand to maintain system efficiency and stability. Goal. This research aims to optimize the locations and sizes of DG units to reduce power losses and optimize voltage profile, taking into account changes in loads hourly over a 24-hour period. Methodology. The study analyzes 24 hourly scenarios using 2 optimization techniques: the conventional particle swarm optimization (PSO) algorithm and the hybrid-dynamic PSO algorithm. A multi-objective function is adopted to reduce power losses and improve voltage profile at the same time. Results. The modified IEEE 33 bus system was used to verify the effectiveness of the proposed method. The hybrid-dynamic PSO algorithm has shown superior performance in reducing active and reactive losses compared to the traditional algorithm. It also contributed to a significant improvement in the voltage profile, demonstrating its high efficiency in dealing with changes in loads demand during time. Scientific novelty of this work lies in the integration of hourly load changes into the process of allocating DG units and using a hybrid-dynamic PSO algorithm that combines the benefits of PSO traditional and adaptation mechanisms, leading to realistic and more efficient improvement. Practical value. This methodology enhances the performance of the smart grid by reducing power losses and voltage deviation under daily load, ultimately reducing operational costs and improving grid reliability. References 28, tables 4, figures 10.

The issues regarding generation uncertainties associated with wind energy and solar photovoltaic (PV) systems along with load demand uncertainties are considered in this paper to evaluate the maximum penetration of renewable energy resources. The nodes which are less voltage stable are considered as the most suitable locations for distributed generations (DGs) placement. For identification of these critical nodes, a voltage stability index (VSI) has been utilized. To analyze the voltage profile, power losses and system voltage stability with large penetration of the wind energy and solar PV into the distribution networks, a probabilistic-based approach has been adopted. The penetration limit depends upon the type of DG that is connected to the distribution network. Usually, the integration of DGs reduces the power losses in the network, however as penetration level increases, the power losses begins to increase. The detailed mathematical models of wind and solar PV-based renewable resources are used. The Hong’s $$2m+1$$ point estimation method combined with Cornish–Fisher expansion is adopted in this paper to conduct the probabilistic studies. The effectiveness of the method is validated through IEEE 33-node radial distribution test network for four different scenarios. The results obtained have been verified and compared with Monte Carlo simulation technique.

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.

Proliferation of electric demand and congestion in the existing network intensify challenges and problems for researchers and power system planners. Currently, these problems are mostly solved by Distributed Generation (DG). DG is placed near the customer end to meet the load demand economically, hence reducing the existing network burden. This paper attempts to present an overview of technological development carried out in the DG field. Numerous technical constraints must be considered while integrating DG in the system. The improvement in voltage stability, voltage profile, and reduction in power losses are the main benefits posed by DG integration. This paper also highlights the key issues associated with the DG allocation problem, types of DG, and methodologies for optimal DG units allocation.

This paper introduces a smart coordinated allocation of distributed generation (DG) units, shunt-connected capacitors (SC), and static VAR compensators (SVC) for power loss minimization. The problem is formulated as a mixed-integer non-linear programming and is solved using a BONMIN solver embedded in General Algebraic Modeling System (GAMS). The proposed method’s evaluation was performed on the IEEE 33-bus test system, where three cases were discussed. GAMS/BONMIN was used to find optimal locations and power outputs of DGs in the first case. Simulation results, in this case, showed that losses were reduced by 48.42% with one DG unit, 58.58% with two DG units, and 65.44% with three optimally allocated DG units. In the second case, the proposed method was used to coordinate DGs and SCs, while in the third case, coordinated allocation of DG and SVC was investigated. The results showed that SC/SVC coordination further reduces active power losses. With three SCs allocated, the reduction in the active power losses was 93.5% compared to the base case, and 82.18% compared to losses in the case where only 3 DGs are connected, while in the case of three SVC allocated, it was 93.85% compared to the base case, and 82.21% compared to the case where 3 DGs are connected. The second and third cases also showed that the coordination of SC/SVC leads to an improved voltage profile, where the bus voltages are close to the acceptable range, and voltage fluctuation is significantly reduced. The comparison between different non-linear programming solvers embedded in GAMS for solving optimal power flow was also presented.

A reliability-based stochastic planning framework for AC-DC hybrid smart distribution systems

A non-simulation-based linear model for analytical reliability evaluation of radial distribution systems considering renewable DGs

Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks

In recent years, utilization of wind-based distributed generation (DG) as one of the most widespread used types of green generation technologies has attracted remarkable consideration. Recently, several contributions have accomplished in case of wind-based generation. In this research, in order to minimize the costs of annual energy losses and Energy Not Supplied (ENS) of the distribution networks, a multi-objective probabilistic based approach is proposed to determine the optimal location and capacity of wind-based units along with providing assurance for a desirable level of voltage profile. An Imperialist Competitive Algorithm (ICA) is employed to overcome non-convexity and complexity of the mixed integer optimization problem of DG allocation in the radial distribution networks. For this purpose, a Fuzzy C-Means (FCM) clustering is used to categorize historical data of load demands and output power of DG units. Then, given the fuzzy center point of the clusters and their respective probability generated from FCM clustering implementation, uncertainty consideration is involved to the probabilistic calculation of both of the wind-based DG and load demand. Moreover, during the calculation process of ENS, different costs of interruption are considered for various customers of the network. Finally, presented technique is exerted for an IEEE 33-bus standard test system subject to the system constrains under different cases and effectiveness and capability of the method is assessed. Obtained results demonstrate competence of the method to produce a significant reduction in ENS value and annual energy losses

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.