Two Stage Methodology for Optimal Siting and Sizing of Distributed Generation in Medium Voltage Network Distribution Generation • Distributed generation, also termed as dispersed generation with small power capacity is directly connected to the distribution network. Unlike conventional central generation. • DG units not only generate part of required load to the nearby consumers but also interchange electrical power with the network. Continues … DGs is rapidly increasing due to their immense • Technical • Economical • Environmental benefits. • Lower investment costs • Short construction times in proximity to load centers, which relieve the network loading • Improve voltage levels • Reduce power losses • Postpone of network upgrading. Overview of Optimization Techniques Several algorithms have been used for DG placement problem such as • Genetic algorithm • Tabu search • Analytical based methods • Heuristic algorithms • Metaheuristic algorithms • Particle swarm PSO. Drawbacks • Heuristic method in selected the appropriate location and determined DG size for minimum real power losses. In spite, the method requires more computational efforts. • Heuristic search requires exhaustive search for all possible locations which may not be applicable to more than one DG. • Genetic algorithm and PSO have been applied to DG placement. Genetic and differential evolutions require preselection of tuning parameters such as population size, cross over and mutation rates. Proposed Technique • Sequential Quadratic Programming (SQP) is used in this paper for sitting and sizing of Distribution Generation. • SQP is an iterative procedure which models the Non Linear Programming (NLP) for a given iteration by an unconstrained Quadratic Programming (QP) sub problem. • By solving this QP sub problem, new updated variables are determined and then would be used to construct a new iteration step. This construction is repeated in such a way that the sequence converges to the minimum value of the objective function. Identification Of Optimal DG Location • The optimal allocation of distributed generation can be obtained by minimizing the total real power loss. The objective function of losses can be represented in N-bus distribution network. • SLi is complex load power • R1i(j) is the equivalent resistance between slack bus 1 and bus i when DG is located at bus j, j is not equal to1. Continues ……. Determined from impedance matrix as follows: • Z11, Zii, and Z1i are the elements of impedance matrix Z. • When the DG is located at slack bus 1 (j=1), the objective function will be as follows: • The optimal bus m to locate DG units can be selected by minimizing the objective function. Continues…… Steps to define the suitable DG location can be summarized by: • Calculate the admittance matrix without selecting DG location. • Modify the admittance matrix corresponding to the possible DG locations. • Calculate the impedance matrix and the equivalent resistances for the possible DG locations. • Rank Objective function (fi) values for different DG locations at different grid buses. • Select the optimal bus m according to the minimum value of the objective function. Testing • Distribution test system consists of 42 bus with total load of 31 MW. This test system represents a real distribution network in Kuwait for Alnuzha region. The main feeders are connected to the low voltage bus of 33/11 kV substation. Results Continues….. • Objective function f is evaluated when the DG is connected to each bus, It is observed that the minimum value of the objective function is achieved when the DG is installed in bus number 34. • The next two candidate locations are bus 20 and 9, respectively. Identification Of Optimal DG Sizes Total cost function to minimize DG’s. • Feeders. • Substation investment. • Operating costs including system losses. • Purchasing power from an existing intertie along the whole planning period. Thereby, the objective function used in this paper is as follows: Continues …… The nomenclature of the above mentioned Objective function is: J :objective function • DG :Distributed Generation. • Vij :Maximum voltage drop allowed across feeder ij. • BK :DG unit capacity (MVA). • d :Discount rate. • Cf :Investment cost. • Cr :Operating cost. • Ce :Electricity market price. • Cij :Cost of feeder. • Ciu :Potential transformer u in existing substation i cost. • Cint: Intertie power cost. • M: Number of load buses. • SDG :Power generated from DG. • Sint: Power imported by intertieive • :DG capacity limit (MVA). • u :Transformer in substation i. • Siu :Transformer u in substation i dispatch power. • SS: Number of substation. • t :Incremental time interval. • T: Horizon planning year. • TN :Total number of buses. • TU :Total number of substation transformers. • Zij :Feeder segment impedance. • pf :System power factor. • :DG binary decision variable. • :Feeder ij binary decision variable. • :Transformer u in substation i binary decision variable. • :Intertie binary decision variable. Continues …… Objective function of the total system cost (J) is minimized subject to various operating constraints to satisfy the electrical requirements of the distribution network. Equality constraints : Active Power Inequality constraints are : • Node voltage • Line loading. • Voltage level limits: The bus voltage (Vi) at bus i is restricted between its upper and lower limits (Vimin and Vimax) for all buses as: Reactive Power • (Pint):power extracted from the grid • (PDGT):generated from possible DG units • (PDT):Total demand • Line loading limit: Maximum apparent power flow along the line. Continues ….. • Substation capacity limit: The total power supplied by the substation. • DG active power limits: The active power generated by each DG. • DG reactive power generation limits: The reactive power generated by each DG. • Other constraints may be equality constraints • such as number of DGs, distribution • Transformers • maximum DG power generation (active and reactive). • intertie power capacity. Continues …… Optimization procedures can be summarized as follows: • 1) Set the optimal location buses defined in the first stage of this paper as candidate buses for DG installation. • 2) Formulate the optimization problem as sequential quadratic programming problem using MATLAB toolbox . • 3) Obtain the optimal DG size by minimizing Objective function. • 4) Check the operation constraints. • 5) Determine for optimum DG size the total planning cost, the total losses, and the power supplied from the grid. Results For The Three Study Cases • 3 cases are studied in this paper. In case I only one DG is installed at bus 34. In case II two DGs are installed at buses 34 and 20. But in case III 3 DGs are assumed to be installed at buses 34, 20 and 9. • As the number of DGs installed is increasing the saving is also increased. maximum saving is achieved when the number of DGs is three. Continues …. • The minimum voltage is 0.9946 pu which indicated that in all the cases voltage profile has been improved. It should be noted that the sizes of DGs in PU based on 10 MVA are dependent on the number of DG locations. Conclusion • A two-stage methodology is developed to define the optimal location and to determine the economic size of DGs to serve the peak demand of distribution system. • By the first stage, a single DG placement algorithm is implemented to find the optimal DG locations to minimize the total system losses of the tested distribution system. • The second stage aims to minimize the total system cost. The total system cost function includes both capital investment and O DG costs in addition to the trade-off between generating power from DG and/or purchasing power from the main grid. • By installing DGs at the optimal locations, the total power loss of the distribution system has been reduced, the power flow along the network feeders has been decreased and then the voltage profile of the system is also improved.
