Distributed Generation Planning Research Articles

Hierarchical Agglomerative Clustering Algorithm method for distributed generation planning

Planning of distributed generator (DG) units in the distribution system employing Hierarchical Agglomerative Clustering Algorithm (HACA) is proposed in this paper. The proposed method overcomes the dependency of existing methods of DG placement, either on the entire global preference information or on the experience of the distribution system planner. The proposed method is validated with weighted sum method and its effectiveness is tested using two distribution systems of different size and configuration. The results of the simulation study demonstrate the suitability of proposed method in solving the problem involving multiple objectives in DG planning studies.

State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization

It is difficult to estimate how much distributed generation (DG) capacity will be connected to distribution systems in the coming years; however, it is certain that increasing penetration levels require robust tools that help assess the capabilities and requirements of the networks in order to produce the best planning and control strategies. The work of this Task Force is focused on the numerous strategies and methods that have been developed in recent years to address DG integration and planning. This paper contains a critical review of the work in this field. Although there have been numerous publications in this area, widespread implementation of the methods has not taken place. The barriers to implementation of the advanced techniques are outlined, highlighting why network operators have been slow to pick up on the research to date. Furthermore, key challenges ahead which remain to be tackled are also described, many of which have come into clear focus with the current drive towards smarter distribution networks.

A cost-effective approach for fast voltage recovery of power distribution networks with renewable energy

This paper presents a cost-effective reactive power planning for active distribution networks based on a new index called reactive power loadability (Q-loadability). The impact of high renewable energy penetration on the static voltage stability of the system is analysed. The effect of load models in distributed generation planning is also demonstrated through nonlinear simulations. In order to ensure fast voltage recovery after a sudden disturbance, compensating devices are located using Q-loadability to increase the system voltage stability limit. Both static and dynamic analyses are carried out to determine the size of distribution static synchronous compensator (D-STATCOM). The parameters of the controller are also tuned to reduce the rating of D-STATCOM. Simulation results show that the proposed approach can reduce the sizes of compensating devices required which, in turn, reduces costs. The effectiveness of the proposed algorithm is verified through several case studies.

Optimal Allocation of Distributed Generation in Micro-Grid Based on the Theory of Life Cycle Cost

In order to solve the problems of the optimal locations, types and sizes of DG (distributed generation) in an islanded micro-grid, which contains wind turbine, photovoltaic, diesel engine, energy storage device and micro gas turbine. The economic model of optimal allocation was built with the objective function that took LCC (life cycle cost) of DGs and active power loss cost into account. The GA (genetic algorithm) was used to optimize the planning of DG and the optimal scheme was attained. The model comprehensively evaluates the economy of the five kinds of DG in the view of LCC theory, and simultaneously realizes the optimal locations, types and sizes considering the differences of DGs and active power loss of the network. Through the case analysis, the results show this model and method can solve the problem of optimal allocation of DG effectively. It is very practical to guide investing and constructing micro-grid for the investors.

Multiple-distributed generation planning under load uncertainty and different penetration levels

The penetration of distributed generation (DG) in power system is continually increasing. Hence, there is a need to investigate the potential benefits and drawbacks of DGs when integrating DG units in existing networks. The challenge of identifying the optimal locations and sizes has triggered research interest and many studies have been presented in this purpose. Different analytical techniques have been developed to minimize power losses for single-DG unit integration. If DG units are integrated at nonoptimal locations, the power losses increase, resulting in increased cost of energy. The novelty of this paper lies in studying the optimal placement of multiple-DG units in order to minimize power losses. In this study, an optimality criterion is investigated to minimize losses by including load uncertainty, different DG penetration levels and reactive power of multiple-DG concept. The simulation results show that it is not possible to form an analytical equation for optimum planning of DG in terms of load distribution, penetration level and reactive power. Due to the complexity of the multiple-DG concept, artificial neural network based optimal DG placement and size method is developed. The proposed method is implemented to the IEEE-30 bus test network and the results are presented and discussed. The results show that the proposed method can be applied to a power network for all possible scenarios.

Distributed Generation Planning: A New Approach Based on Goal Programming

This article proposes a novel methodology that employs a goal programming technique and genetic algorithm for formulation and evaluation of a multi-objective function, respectively, for optimal planning of distributed generator units in the distribution system. The multi-objective function consists of various performance indices that govern the optimal operation of a distribution system with distributed generator units. The proposed method aims to greatly diminish the dependence in existing methods on the global preference information of the distribution system planner by means of simplicity in problem formulation utilizing a goal programming technique. The capacity of the distribution system to accept distributed generator integration is evaluated such that with the placement of every additional distributed generator unit, the value of multi-objective function reduces without any violation in the system operating constraints. The effectiveness of the proposed method is tested using various distribution systems of different sizes and configurations, and the results are validated with the existing methods, namely the iterative genetic algorithm method and the fuzzy embedded genetic algorithm method. Further, different types of distributed generator models are also employed to demonstrate the adaptability of the proposed method in distributed generator planning studies.

Distributed generation planning in deregulated power market - a bibliographical survey

As a result of deregulation, increased use of dispersed generation requires revisiting in management and operation of distribution network. Selection of optimal network topology incorporated with the optimal use of dispersed generation and retail market including real and reactive power. Minimisation of related costs and satisfying technical and physical constraints is also the key issue to concentrate. This paper presents a detailed survey of the work done in the field of DG planning. The task of DG planning can be broadly classified into two sections 1 framing the suitable objective function 2 optimising the objective function. This paper presents papers/literature on both the issues taken into consideration of the deregulated electricity markets. There are nearly 57 citations referenced in this bibliography.

Review of distributed generation planning: objectives, constraints, and algorithms

The Distributed Generation (DG) technologies, which include both conventional and non-conventional type of energy sources for generating power, are gaining momentum and play major role in distribution system as an alternative distribution system planning option. The penetration of DGs is potentially beneficial if distributed generation planning (DGP) is optimal i.e. their site and size are selected optimally by optimization of single or multi-objective function under certain operating constraints. Many researchers have presented some rigorous optimization-based methods for DGP. This paper will review the various objectives, different constraints as well as optimization based models using conventional algorithms, intelligent searches and fuzzy set applications in DGP.

Application of a Modified NSGA Method for Multi-Objective Static Distributed Generation Planning

The characteristics of integrating distributed generation (DG) in a distribution network have changed. These electrical resources are used as an alternative energy source to the main grid. The technical and economical benefits of such units are achieved only when they are optimally sized and placed in the network. In this paper, a static mixed integer non-linear model for distributed generation planning is defined and solved using a modified NSGA method (Non-dominated Sorting Genetic Algorithm). Different DG technologies are considered and the objective functions for minimization are defined as the total active loss, investment and operational costs, and environmental pollution. The method is applied to a test distribution network and the results are discussed in comparison with other methods.

Effect of load models on assessment of energy losses in distributed generation planning

Distributed Generation (DG) is gaining in significance due to the keen public awareness of the environmental impacts of electric power generation and significant advances in several generation technologies which are much more environmentally friendly (wind power generation, micro-turbines, fuel cells, and photovoltaic) than conventional coal, oil and gas-fired plants. Accurate assessment of energy losses when DG is connected is gaining in significance due to the developments in the electricity market place, such as increasing competition, real time pricing and spot pricing. However, inappropriate modelling can give rise to misleading results. This paper presents an investigation into the effect of load models on the predicted energy losses in DG planning. Following a brief introduction the paper proposes a detailed voltage dependent load model, for DG planning use, which considers three categories of loads: residential, industrial and commercial. The paper proposes a methodology to study the effect of load models on the assessment of energy losses based on time series simulations to take into account both the variations of renewable generation and load demand. A comparative study of energy losses between the use of a traditional constant load model and the voltage dependent load model and at various load levels is carried out using a 38-node example power system. Simulations presented in the paper indicate that the load model to be adopted can significantly affect the results of DG planning.

A fuzzy environmental-technical-economic model for distributed generation planning

To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions including economic, technical and environmental attributes. The economic function is the profit of a distribution company (DisCo) from selling the DG output power to its customers. The contribution of this model is the consideration of some DG marginal revenues in the economic function. Inclusion of marginal revenues would not only reduce the investment risks of DG technologies, but also would enable the optimal penetration of DG units. The proposed DG planning framework considers various DG technologies such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), gas turbine (GT) and diesel engine (DE). The system uncertainties (including those for the energy demand, energy price and DG technologies operating and investment costs) are modeled using fuzzy numbers. The numerical case studies have been carried out using the IEEE 37-node distribution test system to demonstrate the performance of the proposed DG planning model.

Fuzzy Embedded Genetic Algorithm Method for Distributed Generation Planning

This article proposes a novel methodology that employs fuzzy set theory and the genetic algorithm for formulation and evaluation of a multi-objective function, respectively, for optimal planning of distributed generator units. The effectiveness of the proposed method is tested using various systems of different sizes and configurations and is validated by comparing the results with that of the weighted sum method. The dependence of existing methods on global preference information of decision maker is overcome in this method. Further, different types of distributed generator models are also employed to demonstrate the importance of the use of a precise distributed generator model and the adaptability of the proposed method in distributed generator planning studies.

Probabilistic dynamic multi-objective model for renewable and non-renewable distributed generation planning

This study proposes a probabilistic dynamic model for multi-objective distributed generation (DG) planning, which also considers network reinforcement at presence of uncertainties associated with the load values, generated power of wind turbines and electricity market price. Monte Carlo simulation is used to deal with the mentioned uncertainties. The planning process is considered as a two-objective problem. The first objective is the minimisation of total cost including investment and operating cost of DG units, the cost paid to purchase energy from main grid and the network reinforcement costs. The second objective is defined as the minimisation of technical risk, including the probability of violating the safe operating technical limits. The Pareto optimal set is found using non-dominated sorting genetic algorithm method and the final solution is selected using a max–min method. The model is applied on two distribution networks and compared with other models to demonstrate its effectiveness.

Uncertainty based distributed generation expansion planning in electricity markets

Decision making under uncertainty is a difficult task in most areas. The distributed generation expansion planning is certainly one of the problems where it is difficult to find an optimal solution for, due to the existence of various uncertain parameters that affect the optimal solution. This paper presents a distributed generation planning strategy based on the multi-objective decision making (MODM) approach under uncertainties. The proposed strategy achieves a Pareto set of locations, sizes, and typical types, for new DG installations in a case-study power network (as the result of a multi-objective optimization). The technical constraints of DG units are also considered in the MODM process. The effects of the uncertain parameters on choosing the best plan(s) are considered using robustness, flexibility, and risk exposure attributes. This aim is performed based on a composite index which is weighted sum of the three mentioned attributes. This paper considers four categories of uncertain parameters including the peak loading factor, electricity market price, and investment and operating costs for the DG long-term planning.

Normal boundary intersection and benefit–cost ratio for distributed generation planning

AbstractDistributed generation (DG) and its planning are becoming more important in power systems. This paper presents a DG planning strategy based on multi‐objective optimization approaches. The main goal of this paper is to determine a Pareto set of locations and sizes of the new DG units by minimizing different cost functions considering the technical constraints, and then determining the best planning strategy. The Pareto set will help the DG planner make a better decision in choosing the best plan from the Pareto set. The best scheme is chosen by the benefit–cost ratio (BCR). The objectives of a DG planner in this paper are assumed to be the cost of investment and operation, cost of energy purchased, cost of energy losses and the cost of energy not supplied. Two market scenarios for the planner are considered: (1) electricity auctions and (2) bilateral contracts of energy. Several case studies are presented to demonstrate the applicability of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.

Effect of Load Models in Distributed Generation Planning

The effect of load models on distributed generation (DG) planning in distribution system is investigated in this work. It is shown that load models can significantly affect the DG planning. Normally a constant power (real and reactive) load model is assumed in most of the studies. Such assumptions may lead to inconsistent and misleading results about deferral values, loss reduction, payback period, and other subsequent calculations. It has been demonstrated that DG planning based on such assumptions would not be effective after implementation. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. A comparative study of real and reactive power loss, real and reactive power intake at the main substation and MVA support provided by installing DG resources for different type of loads models has been performed.

Evaluation of Configuration Plans for DGs in Developing Countries Using Advanced Planning Techniques

Many developing countries have emphasis on distributed generation (DG) technology for their generation expansion planning. The planning considerations and judicious choice of attributes are dictated by prevailing conditions. The attributes considered are capital costs, energy not served per annum, and profits from injecting power into the grid at peak load, all of which are important for a developing country. The uncertain futures considered are three possible loading conditions, which can be low, medium and high. Different scenarios (plans) are generated by various combinations of configurations. DGs can be configured as stand-alone mode, hybrid operation, or micro-grid formation with or without grid connection. With the increased complexities in DG planning options along with the multiple attributes to be accounted, more sophisticated techniques other than conventional economic analysis are needed to arrive at correct decisions by decision makers. The analytical hierarchy process (AHP) is used for obtaining relative weights in an objective way. Further, the statistical method like interval-based multi-attribute decision making with tradeoff analysis is used for shortlisting the feasible plans and identifying the most appropriate plan. It is proposed to use the weights obtained from AHP for finding the performance efficiencies in data envelopment analysis (DEA) for evaluating the plans. A new composite utility function is proposed to resolve cases where performance efficiency is insufficient for evaluation in DEA application. The sample system is derived with reference to a rural electrification scheme in India. The assessment of plans is presented and discussed. The comparative strengths and weaknesses of the methods are reported on the basis of the results obtained.