Distributed generation vs bulk power transmission

The need for independent power generation has increased in recent years, especially with the growing demand in microgrid systems. In a microgrid with several generations of different types and with all kinds of loads of variable nature, an optimal power balance in the system has to be achieved. This optimal objective, which results in minimal energy losses over a specific period of time, requires an optimal location and sizing of the distributed generations (DGs) in a microgrid. This paper proposes a new optimization method in which both optimal location of the DGs and their generation profile according to the load demand profile as well as the type of DG are determined during the life time of the DGs. The types of DGs that are considered in this paper are diesel generators and wind turbine. The method is based on simultaneously minimizing the cost of the investment and operation of the DGs, the cost of power delivered by the the external grid as well as the cost of power losses in the network. The proposed method is tested on the IEEE standard radial distribution network considering time-varying loads and the wind speed every hour of a day.

To manage the microgrid effectively, it is important to get the power information among multiple distributed generation (DG) units. However, when the droop control method is adopted, as the corresponding DG units cannot be simply modeled as VF or PQ buses, the conventional power flow algorithm may become inapplicable. To solve this issue, a time domain iteration (TDI) based power flow algorithm is hereby proposed. Firstly, a microgrid model is prepared, containing a network model and several DG unit models. Then, the proposed TDI is executed for power flow calculation, which mimics the real-time operation of microgrids. In each iteration, the DG unit models input voltages and currents to the network model; then the network model changes its state accordingly and feeds related parameters back to those DG unit models. As the DG unit models simulate the behavior of actual DG units, the proposed algorithm is not limited to the droop control governed microgrids. Moreover, as the TDI has definite physical meaning, convergence problems can be avoided. Finally, the validity of the proposed power flow algorithm is verified through the Matlab simulation results from an 8-bus microgrid system.

This paper proposes a method for determining the optimal sites and sizes of multi-type distributed generations (DG) and capacitors for minimizing reactive power losses (RPL) in distribution systems. The proposed method is developed based on generic closed-form analytical expressions for calculating optimal sizes of DG units and capacitors at their candidate sites. The reduction in RPL with DG and capacitors is evaluated using another analytical expression that relates power injections of DG and capacitors with RPL. An optimal power flow algorithm (OPF) is incorporated in the proposed method to consider the constraints of the distribution systems, DG, and capacitors. Various types of DG are considered, and their optimal power factors can be accurately computed while optimizing the sizes of capacitors in a simultaneous manner to reduce RPL. The 69-bus distribution system is used to test the proposed method. An exact search method is employed to verify the accuracy of the proposed method. The effectiveness of the proposed method is demonstrated for solving the optimal allocation problem with different combinations of multi-type DG units and capacitors.

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.

An overview of microgrid protection methods and the factors involved

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 proposes a refined bacterial foraging algorithm (RBFA) for solving the multi-objective based optimal power dispatch with optimal placement of distributed generation (DG) to minimize the total real power loss, generation cost, the environmental emission and considering various controls and limits. The RBFA is based on the social foraging behavior of the Escherichia coli bacteria and its improved version of the basic bacterial foraging algorithm. The RBFA provides natural selection to eliminate poor foraging strategies for bacteria and to propagate other successful foraging strategies where foraging is proceeded using a position updating process, step length, search dimension and search direction with adaptation of basic foraging principles. Initially, the algorithm randomly generates the particle positions representing the size and location of DG and its proposal to solve the simultaneous optimization of the multi-objective problem. The proposed RBFA is used to determine the optimal sizes and locations of multi-DGs; the different types of DG are considered and the load flow is used to calculate the exact loss and to minimize simultaneously the economic cost and the emission of thermal units by changing the location and varying the sizes of the DG units. The test results indicate that the RBFA method can obtain better results than similar social behavior algorithm method on the IEEE30-bus system. The results are compared with and without DG units. The proposed method found the optimal location and sizing of DG units with control of the voltage profile, control of the cost of generation and control and reduction of environmental pollution and transmission losses.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

Distributed generation (DG), whose installed capacity is increasing rapidly, can be defined as low rating generation that is neither planned nor dispatched centrally and is usually connected to the distribution network. Appropriate control of DG can improve the performance of DG units without violating network constraints, and facilitate the effective participation of DG in power system and market operation. Two control levels, DG unit control and network control, are summarized. DG unit control is introduced based on three technologies: induction generators, synchronous generators and power electric converters. Effective network control can be built based upon active management concept. Finally, three DG control paradigms, MicroGrid, cell and virtual power plant, are discussed.

This paper presents a dynamic droop load sharing scheme based on the available generation capacity of the distributed generation (DG) units. Since conventional droop schemes share loads proportional to units' ratings, they suffer from the inability to maintain an efficient operating point when their input renewable power varies, without imposing their new operating point on other connected DGs in the microgrid. This problem is mainly due to the insensitivity of the droop scheme to the varying nature of the renewable resources used, including wind and solar photovoltaic (PV). A control method is proposed for PV systems; however, it is applicable for all types of droop-controlled renewable DG. A stability analysis of the proposed scheme on DG units is also presented to identify theoretical and practical limits. The proposed scheme identifies the dc operating zone of the inverter-based source as irradiance level changes and conditions the droop parameters appropriately for an efficient load sharing based on available generation, while the rating of each unit is also taken into account. The proposed scheme provides energy saving, since energy demand from a local auxiliary generator is reduced. The proposed method is validated using MATLAB/SIMULINK simulations.

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.

Recent deployment of distributed generation has led to a revolution in distribution systems and the emergence of “smart grid” concepts. The smart grid mainly intends to facilitate integration of renewable energy sources and to achieve higher system reliability and efficiency. Different distributed generation types found in modern distribution systems can be classified into two main categories: intermittent- and dispatchable-based distributed generation; the latter has a unique capability of enabling successful islanding operation, thus improving system reliability. This article proposes a methodology for allocating dispatchable distributed generation units in distribution systems to economically improve system reliability. Distributed generation installation and operation costs are to be optimized against the reliability value, expressed as customers’ “willingness to pay” (WTP) to avoid power interruptions. Therefore, the main goal of this research work is to determine the optimal combination between distributed generation units to be installed and loads to be shed during all possible contingencies. A probabilistic load model is adopted to determine the optimal size of allocated distributed generation units.

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

The author worked on distributed generation (DG) connected to the transmission and distribution GRIDS of Ontario Hydro, from 1985 to 1999. During that period, he has experienced lots of technical problems in connecting to the GRID. In this paper, he would discuss his experiences with the following topics: 1) Types of DG's, including sources of prime power (e.g. water, gas, wind, solar, etc.) in brief 2) Methodology of connection to the GRID minimizing the cost impact to the customers 3) system groundings and transformer connections 4) faults and their effects 5) protective relaying 6) control and metering 7) harmonics and resonance 8) Benefits of DG connecting to the GRID and operating in the micro-GRID format The paper will be concluded with a case study of connecting a DG to the GRID, and conclusions.