Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Summary This paper presents a mixed integer nonlinear programming (MINLP) model to solve the conductor gradation optimization problem of radial distribution systems. The conductor size selection and capacitor allocation problem are solved using exact AC load flow equations. The dynamic load characteristics have been considered in this model. The main objective is to realize both the thermal limit constraints and the bus voltage limit constraints. This is done while minimizing the capital cost of the conductors, the capacitors costs, and the energy loss cost of the different conductors. A 117‐bus radial feeder system has been considered to verify the presented nonlinear model.

Section: Introductionmentioning

confidence: 99%

Optimal conductor selection and capacitor placement in radial distribution system using nonlinear AC load flow equations and dynamic load model

Farrag

Khalil

Omran

2020

“…Literature suggests that BES gives promising solutions in applications like load shifting, 8 variable RES handling 9,10 and system stability improvement 11 . In Reference 12 UC of power system considering thermal units and BES is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…5 The next ESS in the hierarchy of size is BES which offers less polluting, high efficiency, low maintenance and fast operating options. 7 Literature suggests that BES gives promising solutions in applications like load shifting, 8 variable RES handling 9,10 and system stability improvement. 11 In Reference 12 UC of power system considering thermal units and BES is proposed.…”

mentioning

confidence: 99%

Stochasticsecurity constrained unit commitmentwith battery energy storage and wind power integration

Gupta

Jain

Kalkhambkar

et al. 2020

Large penetration of intermittent wind power creates reliability and system security issues in power system operation which can be alleviated by providing energy storage systems (ESS). In this view, this paper proposes stochastic security constrained unit commitment (SCUC) framework with battery energy storage (BES) considering wind power uncertainty and N-1-line contingency criteria. The uncertain wind power is modeled through probable scenarios. In addition, use of N-1 security criteria for pre and post line contingency is modeled using DC power flow. Conventionally, injection shift factor (ISF) are used in power flow equations however, N-1 security formulation is computationally heavy and takes huge time. This work proposes the use of line outage distribution factor (LODF) to reduce the number of non-zero coefficients of post contingency DC security constraints for improving computational requirement. Mathematical model is complex mixed integer linear programming (MILP) problem and solved using benders decomposition for modified IEEE reliability test system and 118-bus system. The results analyze the impact of BES, total operating costs with LODF and ISF, locational marginal price (LMP), wind curtailment, BES scheduling and computation time. The proposed model is economically beneficial for power system operations under large wind penetration. K E Y W O R D S battery energy storage, line outage distribution factor, security constrained unit commitment and wind integration 1 | INTRODUCTION Renewable integration is increasing at a faster rate owing to its environmental benefits and negligible operational cost. However, large plants like wind farms are located at the remote geographical area with limited network capability and low nearby loads. High wind penetration in such regions may produce transmission congestion. Also, large uncertainty

“…With the objective to cost-effectively integrate high shares of stochastic electricity generation (mainly wind and photovoltaic) in modern power systems, new flexible solutions are currently emerging. 1,2 In particular, new concepts of gravity energy storage (GES) are coming to light, whose basic principle is to store energy under the form of gravitational potential energy. Gravity-based storage is often associated with pumped hydro energy storage (PHES), an effective solution that currently covers more than 95% of worldwide capacity.…”

Section: Introductionmentioning

confidence: 99%

Sizing of underwater gravity storage with solid weights participating in electricity markets

Toubeau

Ponsart

Stevens³

et al. 2020

This paper investigates the techno-economic feasibility of the innovative concept of gravity energy storage, where heavy weights are raised and lowered in a water environment. Such eco-friendly systems can be implemented in existing flooded pits or quarries, by leveraging the important depth of these cavities. Moreover, in addition to their long lifetime, they have no visual impact on the landscape, and offer a lot of flexibility to the power system. In this work, we firstly present an analytical study of the storage solution, which allows deriving tractable mathematical constraints describing its operation, such as its nonlinear speed-power curves in both charge and discharge modes. These constraints are then integrated into the investment strategy of a merchant unit that seeks to maximize its profit by jointly participating in the energy and secondary reserve markets. The model is formulated within a stochastic framework to ensure robustness of sizing decisions in view of future market uncertainties. Results from a practical case study (on a natural cavity of 200 m) show that underwater gravity storage is a cost-efficient technology that offers payback periods of less than 10 years, mainly due to its intrinsic low capital costs estimated at around 100 €/kWh. List of Symbols and Abbreviations: n blocks , Number of installed blocks; n gm , Number of installed geared induction machines; φ CAPEX , Total investment costs of the storage system, €; φ OPEX ω,t , Total profit from storage operation in scenario ω at time t, €; n blocks ω,t , Number of blocks stored within the storage system in scenario ω at time t; p dis,res ω,t , p ch,res ω,t , Actual output power in discharge (dis) and charge (ch) modes after the real-time activation of reserves in scenario ω at time t, MW; p dis ω,t , p ch ω,t , Output power in discharge (dis) and charge (ch) modes committed in the energy market in scenario ω at time t, MW; p single,Á,res ω,t , Output power per induction machine in discharge (dis) and charge (ch) modes after the real-time activation of reserves in scenario ω at time t, MW; res dis ω,t ,res ch ω,t , Reserve capacity allocated in the upward secondary reserve market in discharge (dis) and charge (ch) mode in scenario ω at time t, MW; v dis ω,t ,v ch ω,t , Aggregated block speed (at the UGES level) in discharge (dis) and charge (ch) mode in scenario ω at time t, m/s; v single,Á ω,t , Block speed per induction machine in discharge (dis) and charge (ch) mode in scenario ω at time t, m/s; z dis ω,t ,z ch ω,t , Binary variables indicating the discharge (dis) and charge (ch) status in scenario ω at time t; Δ t , Optimization step, 1 hour; η dis , η ch , Efficiency of the storage system in discharge (dis) and charge (ch) modes; κ ω,t ∈ [0,1], Activation rate of the upward reserve; λ act ω,t , Price for activation of balancing reserves in scenario ω at time t, €/MWh; λ EN ω,t , Electricity price in the energy-only market in scenario ω at time t, €/MWh; λ res ω,t , Price for availability of reserve capacity in scenario ω at time ...