Distributed generation technologies, definitions and benefits

In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure.

A multiobjective evolutionary algorithm for the sizing and siting of distributed generation

Recent changes in the electric utility infrastructure have created opportunities for many technological innovations, including the employment of distributed generation (DG) to achieve a variety of benefits. After a brief discussion of the benefits, this paper proposes a general approach and a set of indices to assess some of the technical benefits in a quantitative manner. The indices proposed are: 1) voltage profile improvement index; 2) line-loss reduction index; 3) environmental impact reduction index; and 4) DG benefit index. Simulation results obtained using a simple 12-bus test system and a radial system are presented and discussed to illustrate the value and usefulness of the proposed approach.

An approach to quantify the technical benefits of distributed generation

Present renewable portfolio standards are changing power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. With the increase in penetration of PV resources, power systems are expected to experience a change in dynamic and operational characteristics. This paper studies the impact of increased penetration of PV systems on static performance as well as transient stability of a large power system, in particular the transmission system. Utility scale and residential rooftop PVs are added to the aforementioned system to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The studied system is a large test system representing a portion of the Western U.S. interconnection. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance.

Impact of increased penetration of photovoltaic generation on power systems

Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application. This paper presents a new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss. The ABC algorithm is a new metaheuristic, population-based optimization technique inspired by the intelligent foraging behavior of the honeybee swarm. To reveal the validity of the ABC algorithm, sample radial distribution feeder systems are examined with different test cases. Furthermore, the results obtained by the proposed ABC algorithm are compared with those attained via other methods. The outcomes verify that the ABC algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. The ABC algorithm has only two parameters to be tuned. Therefore, the updating of the two parameters towards the most effective values has a higher likelihood of success than in other competing metaheuristic methods.

Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm

Distributed generation (DG) has much potential to improve distribution system performance and it should be encouraged. However, distribution system designs and operating practices are normally based on radial power flows and this creates a special challenge to the successful introduction of distributed generation. This paper has described a few of the issues that must be considered to insure that DG will not degrade distribution system power quality, safety or reliability. This paper focused on radial systems, although some of the issues discussed are common to low voltage distribution networks.

R.W. De Mello

Papers

Determining the impact of distributed generation on power systems. I. Radial distribution systems