Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification

© 2017 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 worksMicrogrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.Peer ReviewedPostprint (author's final draft

System of Systems Based Decision-Making for Power Systems Operation

A modern power system is composed of many individual entities collaborating with each other to operate the entire system in a secure and economic manner. These entities may have different owners and operators with their own operating rules and policies, and it complicates the decision-making process in the system. In this work, a system of systems (SoS) engineering framework is presented for optimally operating the modern power systems. The proposed SoS framework defines each entity as an independent system with its own regulations, and the communication and process of information exchange between the systems are discussed. Since the independent systems are working in an interconnected system, the operating condition of one may impact the operating condition of others. According to the independent systems’ characteristics and connection between them, an optimization problem is formulated for each independent system. In order to solve the optimization problem of each system and to optimally operate the entire SoS-based power system, a decentralized decision-making algorithm is developed. Using this algorithm, only a limited amount of information is exchanged among different systems, and the operators of independent systems do not need to exchange all the information, which may be commercially sensitive, with each other. In addition, applying chance-constrained stochastic programming, the impact of uncertain variables, such as renewable generation and load demands, is modeled in the proposed SoS-based decision-making algorithm. The proposed SoS-based decision-making algorithm is applied to find the optimal and secure operating point of an active distribution grid (ADG). This SoS framework models the distribution company (DISCO) and microgrids (MGs) as independent systems having the right to work based on their own operating rules and policies, and it coordinates the DISCO and MGs operating condition. The proposed decision-making algorithm is also performed to solve the security-constrained unit commitment incorporating distributed generations (DGs) located in ADGs. The independent system operator (ISO) and DISCO are modeled as self-governing systems, and competition and collaboration between them are explained according to the SoS framework

Emission-aware Energy Storage Scheduling for a Greener Grid

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the ACM International Conference on Future Energy Systems (e-Energy 20) June 2020, Australi

Blockchain-Based Water-Energy Transactive Management with Spatial-Temporal Uncertainties

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency. We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-topeer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatialtemporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods.</p

Blockchain-Based Water-Energy Transactive Management with Spatial-Temporal Uncertainties

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency. We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-topeer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatialtemporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods.</p