Planning, operation and flexibility contribution of multi‐carrier energy storage systems in integrated energy systems

Abstract

Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day-ahead scheduling for multi-carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi-carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi-period optimal energy flow problem in multi-carrier energy networks, this study utilises the well-known teaching-learning based optimisation algorithm. The investigated multi-carrier energy system consists of electrical, natural gas and district heating sub-networks in which an ESS is included in the electrical sub-network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively.

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

“…Equations (4) to (16) Optimization horizon considered one day-ahead (t n = 24, d n = 1). Different optimization methods can be employed in order to solve this problem effectively.…”

“…In comparison to [11][12][13][14], this study purposes a market-based operation of ESSs in which the ESSs minimizes day-ahead operation cost of the prosumer. A different study presented an economical approach to define a policy for electricity pricing, which leads to the optimal charge and discharge of ESSs so that a metaheuristic algorithm is used for day-ahead scheduling of multicarrier energy networks [16]. However, they have neglected weather variability in the proposed method, which may affect the optimal result of the system.…”

NG tank contribution in the integrated energy networks

“…where x is the vector of control variables, and g x, t ( ) and h x, t ( ) are the equality and inequality constraints, respectively. The energy system constraints and the correlation of all sub-networks with each other thorough the linkage components are as mentioned in [10]. Also, F x, t ( ) is the total cost of the energy network that is described by the means of a second-order function as follows:…”

“…Results and discussion: The NG tank model is implemented to a typical integrated energy network including the IEEE 14-bus standard electrical network, the 20-node Belgian natural gas network and the 14-node district heating network. The schematic diagram of these utilised subnetworks is shown in [10]. The NG tank could have many effects on the various parameters of the network.…”

“…Minimise Subject to where is the vector of control variables, and and are the equality and inequality constraints, respectively. The energy system constraints and the correlation of all sub‐networks with each other thorough the linkage components are as mentioned in [10]. Also, is the total cost of the energy network that is described by the means of a second‐order function as follows: In (6), is the c...…”