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

Cost is one of the most essential factors that influence many decisions taken in the distribution system planning. In general, cost can be defined as everything that should be sacrificed to gain the desired results. This paper proposes a new two-stage methodology for distributed generation (DG) placement as an attractive option for distribution system planner. This method aims to minimize cost and maximize total system benefit (TSB). Optimal placement and size are obtained from total cost minimization mathematical problem which is solved in the first stage. For each DG cost characteristics and for each investment payback time, there is an optimal location and size. Then the optimal DG investment payback time results from the TSB maximization problem, which is solved in the second stage. The various DG technologies offer the opportunity of selecting the right energy solution at the right location. Five types of DG are tested to give system deciders some choices. Different system conditions are simulated to illustrate the effect of DG installation on the distribution system as well as the ability of the proposed methodology. A user-friendly software package has been developed to solve efficiently and quickly the two optimization mathematical problems. The proposed methodology has been tested on IEEE 30-bus test system. Copyright © 2010 John Wiley & Sons, Ltd.

The presence of renewable distributed generation (DG) in electrical distribution systems (EDSs) has been increased in recent years, bringing technical, economical, and environmental benefits. However, the stochastic nature of renewable DG units increases the complexity of the planning and operation of EDSs. Hence, advanced planning models that take into account the uncertain nature of the renewable DG units, as well as their benefits in reducing emissions, are required. This work proposes a two-stage stochastic programming model for the expansion planning of EDSs that considers the uncertainties associated with the renewable DG units, the demand, and the energy price. The objective function minimizes the net present value of investments and operation costs, as well as the cost of CO 2 emissions. The proposed model was implemented in the AMPL modeling language and solved via the commercial solver CPLEX. Tests with a 24-node system illustrate the efficiency of the proposed model.

Genetic algorithm based multi-objective reliability optimization in interval environment

The rising penetration of intermittent renewable distributed generation leads to uncertainties in the planning of electric distribution networks. Fully considering the uncertainties pertinent to wind power generation, photovoltaic power generation and load demand, this paper proposes a scenariobased model for the planning of active distribution systems. The solution obtains the optimal capacities and locations of wind and photovoltaic based distributed generators in the distribution system, whilst minimizing the active and reactive power losses as well as voltage deviation. A scenario matrix is generated using the heuristic moment matching technique that captures the stochastic moments and correlation among historical wind and photovoltaic power, and electricity demand. The scenario matrix is then incorporated to propose a stochastic planning model that considers a multi-objective index for minimizing power losses and voltage deviation. Finally, the effectiveness of the proposed planning model is confirmed using case-studies in 53-bus and IEEE 123-bus distribution systems.

Distributed generator-based distribution system service restoration strategy and model-free control methods

Recently the number of distributed generators (DG) being integrated in to the distribution system has increased. The distribution system is no longer a single source system. Thus there has to be a change in strategy for distribution analysis. This would require a fast power flow solution with DG. This paper presents the developed three phase unbalanced power flow algorithm with the choice of modeling DG as PQ or PV node. This gives flexibility in modeling various types of DGs. The distribution system model includes modeling of lines, cables, transformers, switches, capacitors, loads and DGs. IEEE test cases are designed for testing the three phase unbalanced algorithm for single source systems. A DG was introduced in the IEEE 13-node test case and the results demonstrate the effect the DG has on voltage profile and currents. Some of the results were verified using radial distribution analysis package (RDAP)

This paper assesses the impact of conventional and renewable distributed generation (DG) on the reliability of future distribution systems, even when the connection may not be simply radial. The variability of the power output of renewable DGs, such as wind and solar is included. The stochastic nature of the renewable resources and their influence on the reliability of the system are modeled and studied by computing the adequacy transition rate. An integrated Markov model that incorporates the DG adequacy transition rate, DG mechanical failure, and starting and switching probability is proposed and utilized to give accurate results for the DG reliability assessment. The main focus in this paper is conventional generation, solar, and wind DG units. The technique used appears to be applicable to any renewable energy source.

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

Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR‐implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario‐based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed‐integer non‐linear programming (MINLP). A time‐series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario‐based planning method.

The Distributed Generation (DG) or dispersed generation including small size conventional and renewable type of energy sources has gained a great interest in distribution system planning. The integration of DGs into distribution systems achieves numerous technical, economical and environmental benefits and becomes an attractive alternative by future expansion planning of the distribution network. In order to maximize these benefits, it is very important to optimally determine the site and size of the DG units under different operation conditions. This paper presents a two stage methodology for optimal siting and sizing of DGs in medium voltage network. In the first stage, DG placement is selected to reduce the real power losses of the system and to improve its voltage profile. The size of the DG units is determined in the second stage using sequential quadratic programming technique to minimize the total investment and operation cost of the system. The proposed method is tested on real 42 bus test distribution system and the results are presented.

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.

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

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

Distributed Generation (DG) is generally considered as an alternative to bulk power transport. The basic idea is that the presence of electricity generation inside the distribution systems leads to a reduction of the local electricity needs, which consequently leads to a reduced need for power transmission capacity and thus a deferral of investments in transmission lines. However, due to the different operational characteristics of the plethora of types of distributed generation, this hypothesis may prove invalid. Controllable distributed generation, defined as local generation of which the power output can be regulated by the system operator (e.g. stand-alone gas-fired combustion units) will certainly have a positive impact on this direction. However, in reality different types of DG technologies could de implemented in the distribution systems, such as partially or stringently controlled micro-combined heat and power (micro-CHP) units operating according to different local control modes (e.g. thermal-led control) or non-controllable (stochastic) DG units, such as wind power plants. The operation of such units may lead to an opposite effect regarding the necessary transmission capacity. In this paper we first define four types of DG regarding their level of controllability. We then look into the effect on the transmission system of both stringently-controlled DG (i.e. micro-CHP) and stochastic DG (i.e. wind turbines). It is shown that micro-CHP systems may have a positive effect to the dimensioning of the transmission system, while the presence of wind power plants may instead lead to increased investment needs in power transport capacity.

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.