Hossein Akhavan-Hejazi

Optimal Operation of Independent Storage Systems in Energy and Reserve Markets With High Wind Penetration

In this paper, we consider a scenario where a group of investor-owned independently-operated storage units seek to offer energy and reserve in the day-ahead market and energy in the hour-ahead market. We are particularly interested in the case where a significant portion of the power generated in the grid is from wind and other intermittent renewable energy resources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted to a convex optimization problem to be solved efficiently. Our simulation results also show that our design can assure profitability of the private investment on storage units. We also investigate the impact of various design parameters, such as the size and location of the storage unit on increasing the profit.

Energy Storage Planning in Active Distribution Grids: A Chance-Constrained Optimization With Non-Parametric Probability Functions

By considering the specific characteristics of random variables in active distribution grids, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In particular, we develop new closed-form stochastic models for the key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. Different case studies are presented to compare the proposed approach with the conventional deterministic and parametric stochastic approaches, where the latter is based on approximating random variables with Gaussian probability distributions.

Developing a Test Data Set for Electric Vehicle Applications in Smart Grid Research

We analyze a detailed set of driving traces for 536 GPS-equipped taxi vehicles and combine them with the features of four different plug-in hybrid electric vehicle (PHEV) brands that currently dominate the North American market in order to develop a test data set for PHEV-related research in the field of smart grid. Our developed data set is made available to public in [1]. It consists of various information, including but not limited to per-PHEV traces of state-of-charges (SoCs), per-PHEV traces of charging loads at different carefully identified charging stations, per-PHEV information on SoC and charging deadline when the PHEV is parked at a charging station, and some information about the potential of PHEVs for vehicle-to-grid applications.

PEV-based P-Q control in line distribution networks with high requirement for reactive power compensation

While plug-in electric vehicles (PEVs) are expected to provide economic and environmental benefits to the transportation sector, they may also help the electric grid, both as a potential source of energy storage and as a means to improve power quality and reliability. In this paper, our focus is on the latter, where PEVs offer reactive power compensation using P-Q control at their charger inverters. In this regard, we develop a new optimization-based P-Q control strategy for PEV charging stations to be implemented in line distribution networks that are in great need of reactive power compensation, either because of serving large industrial loads or due to the inductive impact of distribution level wind turbines. Our design is based on a nonlinear power flow analysis, and the design objectives are to perform voltage regulation and demand response. Through various computer simulations, we assess our proposed PEV-based reactive power compensation and compare it with the case where no P-Q control is conducted at PEV charging stations.

A stochastic programming framework for optimal storage bidding in energy and reserve markets

This paper focuses on a scenario where a group of independently-operated investor-owned storage units seek to offer both energy and reserve in the day-ahead as well as the hour-ahead markets. We are particularly interested in the case when a significant portion of the power generated in the grid is from wind and other intermittent renewable energy sources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units. Our design takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted into a convex optimization problem and therefore it can be solved efficiently. Our simulation results show that our design can assure profitability of the private investment on storage units. In particular, our design results in much higher profit compared to a similar but deterministic design that simply uses the expected values of the price parameters.

Battery-assisted distribution feeder peak load reduction: Stochastic optimization and utility-scale implementation

In this paper, a stochastic optimization framework is developed to reduce congestion on distribution feeders using batteries, under offline and online design paradigms. Our design is customized, implemented, tested, and analyzed in a real-world testbed that was built based on a university-utility collaboration in California. Our proposed method seeks to optimize peak load at the feeder while taking into account feeder load uncertainty as well as hardware, utility, and customer constraints. We present both experimental and numerical results. Insightful observations, design trade-offs, and lessons learned are discussed.

A joint bidding and operation strategy for battery storage in multi-temporal energy markets

In this paper, we provide a method to determine the optimal schedule and market bids of a battery storage, to maximize revenues from joint operation in day-ahead/ realtime markets. Our model considers financial risk of revenues in both markets and defines battery optimal bids in the two stages of the market, to obtain maximum profit with controlled risk by adapting the Markowitz portfolio selection theory. In the second stage of our framework, a receding horizon algorithm in real-time, updates the predictions of joint profit as well as financial value at risk, and improves the optimal battery schedule accordingly. Our approach has a key feature of tractability, as it is formulated as a convex problem, by several modelling and relaxation techniques. This model enables us to quantify the trade-off between revenues from each markets and the risk of revenues in return.

Power hardware-in-loop simulation of grid-connected battery systems with reactive power control capability

This paper provides a detailed description of developing a power hardware-in-loop (P-HIL) testbed for the simulation and testing of grid-connected battery systems. The test allows not only analyzing the impact of operating grid-tied batteries on the power grid, but also analyzing less addressed battery operational issues, such as temperature, balance, age, and premature capacity loss, as well as the four quadrant energy storage inverter operation. The testbed architecture, hardware components, software systems, communications, and computer controls are explained. P-HIL model validation is provided for both static and dynamic test cases. The applications and benefits of the developed testbed are investigated to study battery characteristics during charge and discharge cycles, individual cell characteristics and cell imbalance, and impact on the power grid.

Customer-side SCADA-assisted Large Battery Operation Optimization for Distribution Feeder Peak Load Shaving

Built upon real-world supervisory control and data acquisition (SCADA) and other measurements of a featured utility-scale testbed, this paper addresses the participation of customer side battery energy storage in providing peak load shaving at a 12.47 kV distribution feeder. A stochastic optimization-based battery operation framework is developed that enables feeder load peak shaving under offline (day-ahead) as well as online (close-to-real-time) control settings. Both designs work through establishing a secured communications line to the utility’s feeder-level SCADA system. Multiple field experiments are conducted, including a full day test with complete control of a 1 MWh/200 kW battery system, as well as various numerical assessments based upon one year of real feeder data.

Chapter 11 – Operation of Independent Large-Scale Battery-Storage Systems in Energy and Reserve Markets*

In this chapter, we consider a scenario where a group of investor-owned independently-operated storage units seek to offer energy and reserve in the day-ahead market and energy in the hour-ahead market. We are particularly interested in the case where a significant portion of the power generated in the grid is from wind and other intermittent renewable energy resources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted to a convex optimization problem to be solved efficiently. Our simulation results also show that our design can assure profitability of the private investment on storage units. We also investigate the impact of various design parameters, such as the size and location of the storage unit on increasing the profit.