Transmission expansion planning (TEP) is generally determined by peak demands. To improve the efficiency and sustainability of energy systems, attention has been paid to demand response programs (DRPs) and distributed generation (DG). DRPs and DG will also have significant impacts on the controllability and economics of power systems, from short-term scheduling to long-term planning. In this study, a non-linear economic design for responsive loads is introduced, based on the price flexibility of demand and the customers' benefit function. Moreover, a probabilistic multi-objective TEP model which considers DRPs is also proposed. A probabilistic analysis method, the so-called Monte-Carlo simulation method, is implemented to handle the uncertainty of the loads, DRPs and DG in the TEP problems. Due to the problems' non-convex formulations, a non-dominated sorting differential evolution program is used to solve the TEP problems. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The planning methodology is then demonstrated on an IEEE 118-bus system in order to show the feasibility of the proposed algorithm.

Microgrids (MGs) and their enabling technologies (e.g. small-scale renewable energy generation, energy storage systems, demand response, and information and communication systems) have attracted increasing attention in the past few years as they are expected to play an important role in future sustainable energy systems. There is a significant research gap in how to plan and manage energy systems with growing numbers of MGs. In this study, an energy system expansion planning (EP) model is used to investigate the quantitative impacts of MGs on energy system sustainability. The EP design problem is formulated as a multi-objective optimisation problem with a range of technical constraints such as AC power flow, reliability, and power quality constraints, as well as including variable and fixed costs. Several case studies are undertaken on electricity networks in New South Wales, Australia. The results confirm that MGs can significantly improve a system's efficiency. However, this efficiency improvement is influenced by factors such as the ratio of the MG participation, the network topology, and other specific power system constraints.

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The increases in renewable current sources, prosumers and decentralised control generation in centralised grids have increased the fluctuations in electricity costs, increased the bi-direction power flow problems and changed the operation and investment of the centralised grid. These new constraints have to be observed to manage the design of the market, the new management near the load and the new operators for the power system. This study proposes a stochastic framework for the centralised grid with a market-based, decentralised management and bi-directional power flow of mixed generators of electrical energy. A decentralised and bi-directional market-based management system (DBMBMS) model is developed which considers the operation costs, security and reliability of the centralised grid, the spot market price, weather changes and the fluctuations in the load. A differential evolution technique with a Monte Carlo program is used in aggregation with bi-directional power flows to find the optimal solutions, depending on the uncertainties of the centralised grid. Using a DBMBMS model, optimal load and price management are then realised, based on the decision-maker's choices. The impacts of this new management system on the reduction of the total electricity prices of the different power sources are analysed and illustrated with practical case studies.

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