Decentralized energy management of power networks with distributed generation using periodical self-sufficient repartitioning approach

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a decentralized model predictive control (MPC) method as the energy management strategy for a large-scale electrical power network with distributed generation and storage units. The main idea of the method is to periodically repartition the electrical power network into a group of self-sufficient interconnected microgrids. In this regard, a distributed graph-based partitioning algorithm is proposed. Having a group of self-sufficient microgrids allows the decomposition of the centralized dynamic economic dispatch problem into local economic dispatch problems for the microgrids. In the overall scheme, each microgrid must cooperate with its neighbors to perform repartitioning periodically and solve a decentralized MPC-based optimization problem at each time instant. In comparison to the approaches based on distributed optimization, the proposed scheme requires less intensive communication since the microgrids do not need to communicate at each time instant, at the cost of suboptimality of the solutions. The performance of the proposed scheme is shown by means of numerical simulations with a well-known benchmark case. © 2019 American Automatic Control Council.Peer ReviewedPostprint (author's final draft

A resilient approach for distributed MPC-based economic dispatch in interconnected microgrids

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Economic dispatch of interconnected microgrids that is based on distributed model predictive control (DMPC) requires the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might not comply with the decisions computed by performing a DMPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed and studied in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.Peer ReviewedPostprint (author's final draft

Challenges to the Integration of Renewable Resources at High System Penetration

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches

Reliability Studies of Distribution Systems Integrated with Energy Storage

The integration of distributed generations (DGs) - renewable DGs, in particular- into distribution networks is gradually increasing, driven by environmental concerns and technological advancements. However, the intermittency and the variability of these resources adversely affect the optimal operation and reliability of the power distribution system. Energy storage systems (ESSs) are perceived as potential solutions to address system reliability issues and to enhance renewable energy utilization. The reliability contribution of the ESS depends on the ownership of these resources, market structure, and the regulatory framework. This along with the technical characteristics and the component unavailability of ESS significantly affect the reliability value of ESS to an active distribution system. It is, therefore, necessary to develop methodologies to conduct the reliability assessment of ESS integrated modern distribution systems incorporating above-mentioned factors. This thesis presents a novel reliability model of ESS that incorporates different scenarios of ownership, market/regulatory structures, and the ESS technical and failure characteristics. A new methodology to integrate the developed ESS reliability model with the intermittent DGs and the time-dependent loads is also presented. The reliability value of ESS in distribution grid capacity enhancement, effective utilization of renewable energy, mitigations of outages, and managing the financial risk of utilities under quality regulations are quantified. The methodologies introduced in this thesis will be useful to assess the market mechanism, policy and regulatory implications regarding ESS in future distribution system planning and operation. Another important aspect of a modern distribution system is the increased reliability needs of customers, especially with the growing use of sensitive process/equipment. The financial losses of customers due to industrial process disruption or malfunction of these equipment because of short duration (voltage sag and momentary interruption) and long duration (sustained interruption) reliability events could be substantial. It is, therefore, necessary to consider these short duration reliability events in the reliability studies. This thesis introduces a novel approach for the integrated modeling of the short and long duration reliability events caused by the random failures. Furthermore, the active management of distribution systems with ESS, DG, and microgrid has the potential to mitigate different reliability events. Appropriate models are needed to explore their contribution and to assist the utilities and system planners in reliability based system upgrades. New probabilistic models are developed in this thesis to assess the role of ESS together with DG and microgrid in mitigating the adverse impact of different reliability events. The developed methodologies can easily incorporate the complex protection settings, alternate supplies configurations, and the presence of distributed energy resources/microgrids in the context of modern distribution systems. The ongoing changes in modern distribution systems are creating an enormous paradigm shift in infrastructure planning, grid operations, utility business models, and regulatory policies. In this context, the proposed methodologies and the research findings presented in this thesis should be useful to devise the appropriate market mechanisms and regulatory policies and to carry out the system upgrades considering the reliability needs of customers in modern distribution systems

Resilient Distributed Energy Management for Systems of Interconnected Microgrids

In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201

MORE THAN SMART: A Framework to Make the Distribution Grid More Open, Efficient and Resilient

This paper is the result of a series of workshops with industry, government and nonprofit leaders focused on helping guide future utility investments and planning for a new distributed generation system. The distributed grid is the final stage in the delivery of electric power linking electricity sub-stations to customers. To date, no state has initiated a comprehensive effort that includes the planning, design-build and operational requirements for large scale integration of DER into state-wide distributed generation systems. This paper provides a framework and guiding principles for how to initiate such a system and can be used to implement California law AB 327 passed in 2013 requiring investor owned utilities to submit a DER plan to the CPUC by July 2015 that identifies their optimal deployment locations

A genetic algorithm approach for the identification of microgrids partitioning into distribution networks

In this paper a Genetic Algorithm (GA) is used to partition a distribution network with the aim to minimize the energy exchange among the microgrids (i.e. maximize self-consumption) in presence of distributed generation. The proposed GA is tested on the IEEE prototypical network PG & E 69-bus. The microgrid partitioning is tested over a period of one year with hourly sampled data of real household consumption and real distributed generation data. The proposed GA approach is compared with a Tabu Search (TS) method already presented in the scientific literature. Results show that both GA and TS lead to the identification of equivalent microgrids. However, the GA based approach achieves better convergence results allowing for a reliable network partitioning with less CPU effort. Moreover, the histograms of the power unbalances of the microgrids show unimodal and skewed distributions offering an interesting starting point for the appropriate deployment of storage and control systems