Unified electrical and thermal energy expansion planning with considering network reconfiguration

Abstract

Simultaneous expansion of the electrical and thermal energies collected with conventional expansion options is scrutinised. A robust, bio-inspired evolutionary optimisation method is proposed, to handle the complex expansion planning of a system consisting of both electrical and thermal forms of energy. Rewiring, network reconfiguration, installation of new lines and also new electrical and thermal generation units are considered as the traditional alternatives in expansion planning. To solve the problem, overall generation requirements of a network are assigned along the planning horizon. The allocation problem is formulated as a mixed-integer non-linear programming problem that minimises the overall system cost owing to generation capacity among the grid nodes and the newly added or upgraded lines. The performance of the original shuffled frog leaping (SFL) optimisation algorithm is advanced to overcome the complexity of the proposed expansion planning problem. Two modification steps were added to the original SFL technique to enable the proposed modified SFL algorithm to extricate from local minima. The two modification phases pledge a fast convergence rate by achieving a rapid adaptive algorithm, besides a better diversification which is the key to extricate from local minima. The efficacy and robustness of the proposed methodology are verified by applying the method to two modified standard test systems. NomenclatureSymbol and description P Line ij, min minimum active power between ith and jth bus P Line ij expected value of active power between ith and jth bus P Line ij, max maximum active power between ith and jth bus V min minimum value of voltage magnitude V i amplitude of the voltage at the ith bus V max maximum value of voltage magnitude δ i angle of the voltage at the ith bus Y ij amplitude of admittance between the ith and jth buses θ ij angle of branch between the ith and jth buses N P number of power units N C number of combined heat and power (CHP) units N h number of heat units N L number of lines C Elec.

Resilience‐based tri‐level framework for simultaneous transmission and substation expansion planning considering extreme weather‐related events

“…In [29], a cost-driven probabilistic model was expressed for fortifying the power transmission network infrastructure against the EWEs through a hybrid of a differential evolution algorithm and a Monte-Carlo simulation method. Reinforcing the power distribution network infrastructure with the consideration of small-scale thermal generation units was carried out by a shuffled frog leaping optimisation algorithm in [30] and a honey bee mating optimisation algorithm in [31]. In [32], a leastcost linearised static model was represented for efficiently augmenting the power transmission lines using a piecewise McCormick relaxation technique.…”

“…Constraints (27) and (28) indicate the maximum permissible number of newly installed transmission switches in each corridor and in all corridors of the transmission network during the planning horizon, respectively. The maximum admissible number of newly installed transmission substations on each bus of the transmission network at period t and at all periods of the planning horizon is addressed by constraints (29) and (30), respectively. The maximum permissible number of existing augmented transmission substations on each bus of the transmission network at period t and at all periods of the planning horizon is represented by constraints (31) and (32), respectively.…”

Short‐term planning model for distribution network restructuring based on heat maps

“…The expanded distribution network planning can be achieved by power network restructuring based on the existing devices [7]. Abbasi and Seifi [8] optimise the simultaneous expansion of electrical and thermal networks by considering network reconfiguration. In the second category, the efficiency of the devices is enhanced by prioritising distribution network restructuring.…”

A Multi-Energy System Expansion Planning Method Using a Linearized Load-Energy Curve: A Case Study in South Korea

“…In [17], the integrated expansion planning method was applied in a case study on the Alberta region (Canada), where many CHP units might be used. Furthermore, other models detailed in [18,19] were designed to minimize energy loss and voltage fluctuation, as well as cost. The study in [20] proposed an expansion planning model for distributed multi-energy generation to cope with long-term uncertainty, including operation and investment flexibility.…”