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The many well-established advantages of distributed generation (DG) make their usage in active distribution networks prevalent. However, uncontrolled operation of DG units can negatively interfere with the performance of other equipment, such as tap-changers, in addition to resulting in sub-optimal usage of their potential. Thus, adequate scheduling/control of DG units is critical for operators of the distribution system to avoid those adverse effects. A linearised model of a multi-objective method for coordinating the operation of photovoltaics, battery storage systems, and tap-changers is proposed. Three objective functions are defined for simultaneously enhancing voltage profile, minimising power losses, and reducing peak load power. The formulated multi-objective problem is solved by means of the epsilon-constraint technique. A novel decision-making methodology is offered to find the Pareto optimality and select the preferred solution. To assess to proposed model's performance, it is tested using 33-bus IEEE test system. Consequently, tap-changers suffer lessened stress, the batteries state-of-charge is kept within adequate limits, and the DG units operation is at higher efficiency. The obtained results verify the effectiveness of this approach.

Recently, demand-response (DR) programmes are one of the appropriate tools for energy systems to encourage flexible customers to participate in the operation of energy systems. One of the complex tasks in multi-energy environments is optimal energy flow (OEF) problem of these systems. In this regard, this study investigates the OEF of an integrated electrical, heat, and gas system considering flexible heat and electrical demands. The conventional DR programme has been combined with the demand-side energy supplying management activity by introducing switching concept among input energy carriers. The way of the supplying energy of flexible customer can be changed by switching among input energy carriers. Here, the integrated system operator minimises the system operation costs subject to supply flexible consumers’ energy. To solve the complex OEF problem, this study presents a new optimisation algorithm named modified biogeography-based optimisation (BBO) algorithm. In this study, the proposed modification for the original BBO increases the robustness and the capability of the proposed optimisation method. The numerical results show that the flexible DR programme creates smoother energy demand curves in heat and electrical networks and reduce the operating costs of the integrated system.

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

This paper presents a comprehensive model of dynamic optimal operation management of the smart nano-grids (NGs) including the micro wind turbines (WTs) and micro photovoltaics (PVs) as renewable energy sources (RESs) while micro turbines (MTs) and fuel cell (FC) are considered as non-RESs. Furthermore, two types of lead-acid and lithium-ion batteries are considered besides the three types of controllable, curtailment-able and must run loads to increase the flexibility of the proposed formulation. The different objective functions of NG operation problem to be minimised include operating cost, environmental damage cost of pollution gases and exchanged power cost with the main grid. Also the power losses of batteries are modelled using the quadratic functions based on types and output powers of considered batteries, while these losses impose additional cost to operation cost functions of batteries. A modified teaching-learning-based optimisation (MTLBO) algorithm is used to cope with the multiobjective problem considering the constraints.

Here, an optimisation framework is proposed for integrated resource expansion planning (IREP) including conventional generation units, wind generation units, and transmission lines while taking into account the role of demand response program (DRP) aggregators. This problem is a bi-level optimisation problem. In the upper-level problem, the objective function is to maximise the profit for each resource, that is, the generation company (GENCO), wind generation company (WINDCO), and transmission company (TRANSCO) as well as the DRP aggregator. Also, the lower-level problem considers a market model with the participation of private GENCOs, WINDCOs, TRANSCOs, and DRP aggregators. The lower-level problem minimises energy cost subject to AC power flow (PF) equations, power network limitations, pollution constraint, GENCO, WINDCO, TRANSCO constraints, and technical limitations of DRPs. Here, the lower-level problem is non-linear and non-convex. Accordingly, to facilitate the solution of the proposed bi-level optimisation problem, a linear model is proposed. Then, the proposed bilevel optimisation problem is converted into an integrated single-level one using the Karush-Kuhn-Tucker (KKT) conditions. Eventually, a mixed-integer linear programming (MILP) model is proposed. The proposed method is applied to the IEEE 6-bus and the IEEE 30-bus test systems, and finally, the capabilities of the proposed scheme are evaluated.

In this study, a stochastic multiobjective framework is proposed for distribution feeder reconfiguration (DFR). The proposed multiobjective framework can concurrently optimise competing objective functions including total power losses, voltage deviation and total cost. Moreover, system uncertainties including wind power generation and active and reactive load uncertainty are explicitly considered in the stochastic DFR problem. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism in conjunction with Weibull/Gaussian probability distribution function of wind/load forecast variations for random scenario generation wherein the stochastic multiobjective DFR problem is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective formulation based on the adaptive modified particle swarm optimisation (AMPSO) is implemented for each deterministic scenario of the first stage. Utilisation of the stochastic framework would capture more uncertainty spectrum of the investigated multiobjective DFR problem rather than that of the deterministic framework. Consequently, the results of the stochastic framework are more realistic and dependable. Moreover, the new adaptive optimisation algorithm (AMPSO) has much more ability than the other well-known algorithms in the area of optimisation applications.

Fuel cells are environmentally clean, have low emission of oxides of nitrogen and sulfur and, at the same time, they can operate with a very low level of noise. In addition, they can provide energy in a controlled way with higher efficiency compared to conventional power plants. This study presents an efficient multi-objective new fuzzy self adaptive particle swarm optimisation evolutionary algorithm to solve the multi-objective optimal operation management considering fuel cell power plants in the distribution network. The objective functions of the problem are to decrease the total electrical energy losses, the total electrical energy cost, the total pollutant emission and deviation of bus voltages. The proposed algorithm is tested on a real distribution test feeder and the results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions.