PhD Seminar: Electricity Market Participation and Investment Planning Frameworks for Energy Storage SystemsExport this event to calendar

Friday, June 26, 2020 — 2:00 PM EDT

Candidate: Hisham Alharbi

Title: Electricity Market Participation and Investment Planning Frameworks for Energy Storage Systems

Date: June 26, 2020

Time: 2:00 PM

Place: REMOTE PARTICIPATION

Supervisor(s): Bhattacharya, Kankar

 

Abstract:

The recent trend of increasing share of renewable energy sources (RES) in the generation mix has necessitated new operational and planning studies because of the high degree of uncertainty and variability of these sources. RES such as solar photovoltaic and wind generation are not dispatchable, and when there is excess energy supply during off-peak hours, RES curtailment is required to maintain the demand-supply balance. Furthermore, RES are intermittent resources which have introduced new challenges to the provision of ancillary services that are critical to maintaining the operational reliability of power systems. Energy storage systems (ESS) play a pivotal role in facilitating the integration of RES to mitigate the aforementioned issues. Therefore, there is a growing interest in recent years to examine the potential of ESS in the future electricity grids.

 

This research focuses on developing market participation and investment planning frameworks for ESS considering different ownership structures. First, a novel stochastic planning framework is proposed to determine the optimal battery energy storage system (BESS) capacity and year of installation in an isolated microgrid using a novel representation of the BESS energy diagram. The proposed models are developed from the system operator's perspective to ensure that the BESS contributes to microgrid operation via load leveling and reserve provisions in conjunction with the spinning reserve from dispatchable distributed generation units. A decomposition-based approach is proposed to solve the problem of stochastic planning of BESS under uncertainty. The optimal decisions minimize the net present value of total expected costs over a multi-year horizon considering optimal BESS operation using a novel matrix representing BESS energy capacity degradation. The proposed approach is solved in two stages as mixed integer linear programming (MILP) problems to ensure the convergence. The optimal ratings of the BESS are determined in the first stage, while the optimal installation year is determined in the second stage. Extensive studies considering four types of BESS technologies for deterministic, Monte Carlo Simulations, and stochastic cases are presented to demonstrate the effectiveness of the proposed approach.

 

The thesis further studies the investment decisions on BESS installations by a third-party investor in a microgrid. The optimal BESS power rating, energy capacity, and the year of installation are determined while maximizing the investor's profit and simultaneously minimizing the microgrid operational cost. The multi-objective problem is solved using a goal programming approach with a weight assigned to each objective. The BESS is modeled to participate in energy arbitrage and provisions of operating reserves to the microgrid, considering its performance parameters and capacity degradation over the planning horizon.

 

Finally, in the third problem addressed in the thesis in the context of electricity markets, the non-strategic and strategic participation of a pumped hydro energy storage (PHES) facility in day-ahead energy and performance-based regulation (PBR) markets, which includes regulation capacity and mileage, are examined. The PHES is modeled with the capability of operating in hydraulic short-circuit (HSC) mode with detailed representation of its operational constraints, and integrated with an energy-cum-PBR market clearing model. For its strategic participation, a bi-level market framework is proposed to determine the optimal offers and bids of the PHES that maximize its profit. The operation of PHES is modeled at the upper level, while the market clearing is modeled in the lower level problem. The bi-level problem is formulated as a mathematical programming with equilibrium constraints (MPEC) model, which is linearized and solved as an MILP problem. Several case studies are carried out to demonstrate the impact of PHES' non-strategic and strategic operations on market outcomes. Furthermore, stochastic case studies are conducted to determine the PHES strategies considering the uncertainty of the net demand and rivals' price and quantity offers.

Location 
REMOTE PARTICIPATION


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