• Energy Research

The symmetric/constrained fuzzy power flow (SFPF/CFPF) models are suitable to quantify the adequacy of transmission network in satisfying “reasonable demands for the transmission of electricity” as defined, in the European Directive 2009/72/EC. In this paper, SFPF/CFPF is mainly used to identify the basic repressions (inappropriate definition of reasonable demands) of fuzzy data (generation or load) when the adequacy of a transmission system is assessed. This situation arises essentially in cases where the network configuration does not fully support the requested power specifications. It means that these requests are inadequately formulated and lead to the creation of artificial repression in the results (artificial inadequacy of the transmission system). In this article, it is intended to show how these cases can be identified. For this purpose, the SFPF will be used, which does not consider branch limits. With this study, it is also shown how the reference power flows of the system are obtained in order to identify the congested branches.

The symmetric/constrained fuzzy power flow (SFPF/CFPF) models are suitable to quantify the adequacy of transmission network in satisfying “reasonable demands for the transmission of electricity” as defined, in the European Directive 2009/72/EC. In this paper, SFPF/CFPF is mainly used to identify the basic repressions (inappropriate definition of reasonable demands) of fuzzy data (generation or load) when the adequacy of a transmission system is assessed. This situation arises essentially in cases where the network configuration does not fully support the requested power specifications. It means that these requests are inadequately formulated and lead to the creation of artificial repression in the results (artificial inadequacy of the transmission system). In this article, it is intended to show how these cases can be identified. For this purpose, the SFPF will be used, which does not consider branch limits. With this study, it is also shown how the reference power flows of the system are obtained in order to identify the congested branches.

Dynamic economic dispatch (DED) aims to schedule the committed generating units' output active power economically over a certain period of time, satisfying operating constraints and load demand in each interval. Valve-point effect, the ramp rate limits, prohibited operation zones (POZs), and transmission losses make the DED a complicated, non-linear constrained problem. Hence, in this paper, imperialist competitive algorithm (ICA) is proposed to solve such complicated problem. The feasibility of the proposed method is validated on five and ten units test system for a 24 h time interval. The results obtained by the ICA are compared with other techniques of the literature. These results substantiate the applicability of the proposed method for solving the constrained DED with non-smooth cost functions. Besides, to examine the applicability of the proposed ICA on large power systems, a test case with 54 units is studied. The results confirm the suitability of the ICA for large-scale DED problem.

Dynamic economic dispatch (DED) aims to schedule the committed generating units' output active power economically over a certain period of time, satisfying operating constraints and load demand in each interval. Valve-point effect, the ramp rate limits, prohibited operation zones (POZs), and transmission losses make the DED a complicated, non-linear constrained problem. Hence, in this paper, imperialist competitive algorithm (ICA) is proposed to solve such complicated problem. The feasibility of the proposed method is validated on five and ten units test system for a 24 h time interval. The results obtained by the ICA are compared with other techniques of the literature. These results substantiate the applicability of the proposed method for solving the constrained DED with non-smooth cost functions. Besides, to examine the applicability of the proposed ICA on large power systems, a test case with 54 units is studied. The results confirm the suitability of the ICA for large-scale DED problem.

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.

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.

Owing to the local nature of voltage and reactive power control, the voltage control is managed in a zonal or regional basis. A new comprehensive scheme for optimal selection of pilot points is proposed in this study. The uncertainties of operational and topological disturbances of the power system are included to provide the robustness of the pilot node set. To reduce the huge number of probable states (i.e. combined states of load and topological changes), a scenario reduction technique is used. The resulted optimal control problem is solved using a new immune-based genetic algorithm. The performance of the proposed method is verified over IEEE 118-bus and realistic Iranian 1274-bus national transmission grids.

Owing to the local nature of voltage and reactive power control, the voltage control is managed in a zonal or regional basis. A new comprehensive scheme for optimal selection of pilot points is proposed in this study. The uncertainties of operational and topological disturbances of the power system are included to provide the robustness of the pilot node set. To reduce the huge number of probable states (i.e. combined states of load and topological changes), a scenario reduction technique is used. The resulted optimal control problem is solved using a new immune-based genetic algorithm. The performance of the proposed method is verified over IEEE 118-bus and realistic Iranian 1274-bus national transmission grids.