Integrated battery model in cost‐effective operation and load management of grid‐connected smart nano‐grid

Abstract

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.

A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm

“…Then, the lithium ion battery packs can release more energy than usual by considering much about its legitimate application. This is because they have been stored since the last common charging treatment, according to which the above state and observation equations can be recorded as shown in Equation (11).…”

“…The charged state is one of the core state parameters in the management system of the lithium ion battery packs together with its affiliates in the charged state prediction that should be distinguished from other parameters [7][8][9]. Since the battery charged state is a physical quantity that cannot be measured directly [10,11], it should be estimated by the information such as voltage, current, internal resistance and temperature that are related to the operating state of the lithium ion battery packs, so its prediction purpose can be turned into a model parameter reorganization problem according to its special impact on the charged state estimation accuracy [12]. Some adaptive estimation methods have been proposed to be suitable for different application backgrounds of the lithium ion batteries [13][14][15][16], including electric vehicles, substations, smart grids and financial uninterruptible power systems.…”

Optimal day‐ahead scheduling for active distribution network based on improved information gap decision theory

“…The total dispatch period of ADN is set to one day, and the dispatch interval is 1 h. The predicted data of wind power, PV and load are shown in Figure 4. The parameters of microturbine is from [27]. The electricity purchase prices of ADN from the main network using time-sharing electricity prices and the compensated prices of interruptible load are referred from [28,29].…”

“…In the modified IEEE 33-node system, four power sources are added, including two microturbines, one wind turbine, and 4. The parameters of microturbine is from [27]. The electricity purchase prices of ADN from the main network using time-sharing electricity prices and the compensated prices of interruptible load are referred from [28,29].…”