Amir H. Hajimiragha

Trends in Microgrid Control

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

A Robust Optimization Approach for Planning the Transition to Plug-in Hybrid Electric Vehicles

This paper proposes a new technique to analyze the electricity and transport sectors within a single integrated framework to realize an environmentally and economically sustainable integration of plug-in hybrid electric vehicles (PHEVs) into the electric grid, considering the most relevant planning uncertainties. The method is based on a comprehensive robust optimization planning that considers the constraints of both the electricity grid and the transport sector. The proposed model is justified and described in some detail, applying it to the real case of Ontario, Canada, to determine Ontarios grid potential to support PHEVs for the planning horizon 2008-2025.

Mean-Conditional Value-at-Risk Optimal Energy Storage Operation in the Presence of Transaction Costs

This paper addresses the formulation and solution of an optimal energy storage management problem under risk consideration and transaction costs of trading energy with the power grid. The price evolves as a stochastic process, capable of correctly explaining the seasonality effects as well as the tail fatness and spikiness in its distribution. Transaction costs capture the price impact of the storage operation on the electricity spot price. A risk analysis of an optimal risk neutral deterministic policy as well as the simple myopic policy indicates that the realized operational cost may notably differ from the expected cost by a considerable probability. This difference suggests that we need to consider risk. Using the downside risk measure of conditional value-at-risk, an optimal risk averse conversion and transmission strategy, among the grid, the renewable power generation source, and an energy storage is proposed to fully satisfy the electricity demand and minimize the expected operational cost as well as the risk. Our numerical study using data from NYISO demonstrates the impacts of risk consideration and the transaction cost parameters on the optimal strategy structure, its expected cost, and its risk.

Research and development of a microgrid control and monitoring system for the remote community of Bella Coola: Challenges, solutions, achievements and lessons learned

Reliance on costly and polluting diesel generators is a major difficulty common to almost all the remote off-grid communities. However, there are oftentimes opportunities to replace at least a part of it with clean renewable energy. This can be achieved by incorporating appropriate energy storage technologies for shifting the energy as well as smart control and monitoring systems. Bella Coola in British Columbia has been the first remote community in Canada that initiated the replacement of a part of its diesel consumption with clean energy. This paper attempts to describe the technical challenges in Bella Coola, proposed solutions with emphasis on the control and monitoring part, a summary of open and closed-loop tests, and the lessons learned from this microgrid project that can be inspiring for future projects.

Transmission Congestion Relief Using Privately Owned Large-Scale Energy Storage Systems in a Competitive Electricity Market

The trend of integrating more nondispatchable renewable sources into the electric grid and phasing out dispatchable fossil-fueled power plants in the near future reduces the operational flexibility, increases the chance of transmission congestion, and endangers the stability of electric system. Utilities are investigating the application of large-scale energy storage systems (ESSs) to address some of these imminent challenges to their power systems. In this paper, the application of privately owned large-scale ESSs for the purpose of congestion relief in transmission systems as an ancillary service is investigated. It is demonstrated that in conventional optimal dispatch algorithms for an ESS, the storage system cannot effectively contribute to congestion relief since the dispatch algorithm has not prepared the ESS in advance. Hence, a new real-time optimal dispatch (RTOD) algorithm is proposed that aims to generate revenue primarily by exploiting electricity price arbitrage opportunities in the day-ahead electricity market while optimally preparing the ESS to maximize its contribution to congestion relief as an ancillary service. A compressed-air ESS is sized and employed for evaluations of the proposed RTOD algorithm. The efficacy and feasibility of the proposed algorithm are validated using real-world price data from Ontarios wholesale electricity market. An analysis is presented regarding the appropriate amount of financial compensation for the ESS owner due to this contribution to congestion relief in the studied electricity market.

Microgrids Frequency Control Considerations Within the Framework of the Optimal Generation Scheduling Problem

Microgrids are perceived to play an important role in the future of smart grids. Cost reduction and frequency regulation are among the major concerns in this context. This paper aims to study the mutual impacts of these issues and to propose an integrated mathematical framework in order to achieve minimum costs while maintaining frequency-regulation requirements. Inspired by the operational practices and requisites of the real-world microgrid applications, different methods of frequency control are reviewed and discussed. This is followed by developing adequate models for the different types of frequency-regulation mechanisms in the framework of the microgrid generation scheduling problem. The application of the proposed methodology to the Bella Coola microgrid in British Columbia, Canada, is explained, and several numerical results are presented and discussed.

A Real-Time Multistep Optimization-Based Model for Scheduling of Storage-Based Large-Scale Electricity Consumers in a Wholesale Market

A new real-time optimal scheduling model is proposed and analyzed in this paper to aggregate storage benefits for a large-scale electricity consumer. The complete model for optimal operation of storage-based electrical loads considering both the capital and operating expenditures of storage is developed. A real-time load forecaster is incorporated into the optimal scheduling algorithm using soft constraints, slack variables, and penalizing mechanisms. The application of the proposed model to a real-world large-scale electricity consumer is examined and compared with previous models. It is demonstrated that the proposed model outperforms prior models by generating higher profitability of investment in storage, lower storage operating expenditure, and an extended life of the storage plant.

Risk-sensitive stochastic optimization for storage operation management

This study concerns charging/discharging management of an energy storage device, integrated with a renewable energy supply to fully serve loads over a finite time horizon. The goal is to minimize the cost (or maximize the revenue) of exchanging energy with the electrical grid. The storage device is defined through conversion and dissipation losses, capacity limitations, and bounds on conversion rates. A general-purpose rolling horizon procedure is developed, with the goal of optimizing a risk functional of the cost distribution. Our computational results suggest that the general-purpose algorithm for our setting retains the qualitative behavior of the exact optimal control strategy, which is established under special assumptions. The value of the general-purpose algorithm is evaluated for both risk-neutral and risk-averse criteria, with notable improvements reported in the study.

Loss of data management in real-time short-term forecasting algorithms

In this paper, a new technique is proposed to mitigate the problem of data loss in real-time short-term forecasting (STF) algorithms. The proposed method can be applied to any STF algorithm in order to resolve the problem of data loss with minimal additional computation. In addition, the proposed implementation strategy can be employed to address the challenges of handling holidays in the electrical load and energy price forecasting algorithms. In order to evaluate the performance of the proposed method, a well-accepted real-time short-term load forecasting (STLF) method has been implemented. The historical electricity load information and ambient temperature of a real-life load are used in this study. The investigation reveals that the proposed method properly addresses the above-mentioned challenges with high accuracy and minimal additional computation. Although the case study in this paper deals with the practical challenges only in real-time STLF, the proposed method can be used in any real-time STF algorithm, such as renewable power generation and energy price forecasting.