3,072 research outputs found
Adaptive Electricity Scheduling in Microgrids
Microgrid (MG) is a promising component for future smart grid (SG)
deployment. The balance of supply and demand of electric energy is one of the
most important requirements of MG management. In this paper, we present a novel
framework for smart energy management based on the concept of
quality-of-service in electricity (QoSE). Specifically, the resident
electricity demand is classified into basic usage and quality usage. The basic
usage is always guaranteed by the MG, while the quality usage is controlled
based on the MG state. The microgrid control center (MGCC) aims to minimize the
MG operation cost and maintain the outage probability of quality usage, i.e.,
QoSE, below a target value, by scheduling electricity among renewable energy
resources, energy storage systems, and macrogrid. The problem is formulated as
a constrained stochastic programming problem. The Lyapunov optimization
technique is then applied to derive an adaptive electricity scheduling
algorithm by introducing the QoSE virtual queues and energy storage virtual
queues. The proposed algorithm is an online algorithm since it does not require
any statistics and future knowledge of the electricity supply, demand and price
processes. We derive several "hard" performance bounds for the proposed
algorithm, and evaluate its performance with trace-driven simulations. The
simulation results demonstrate the efficacy of the proposed electricity
scheduling algorithm.Comment: 12 pages, extended technical repor
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Robust optimization for energy transactions in multi-microgrids under uncertainty
Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid
A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids
Cooperating interconnected microgrids with the Distribution System Operation
(DSO) can lead to an improvement in terms of operation and reliability. This
paper investigates the optimal operation and scheduling of interconnected
microgrids highly penetrated by renewable energy resources (DERs). Moreover, an
efficient stochastic framework based on the Unscented Transform (UT) method is
proposed to model uncertainties associated with the hourly market price, hourly
load demand and DERs output power. Prior to the energy management, a newly
developed linearization technique is employed to linearize nodal equations
extracted from the AC power flow. The proposed stochastic problem is formulated
as a single-objective optimization problem minimizing the interconnected AC MGs
cost function. In order to validate the proposed technique, a modified IEEE 69
bus network is studied as the test case
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
Online Energy Generation Scheduling for Microgrids with Intermittent Energy Sources and Co-Generation
Microgrids represent an emerging paradigm of future electric power systems
that can utilize both distributed and centralized generations. Two recent
trends in microgrids are the integration of local renewable energy sources
(such as wind farms) and the use of co-generation (i.e., to supply both
electricity and heat). However, these trends also bring unprecedented
challenges to the design of intelligent control strategies for microgrids.
Traditional generation scheduling paradigms rely on perfect prediction of
future electricity supply and demand. They are no longer applicable to
microgrids with unpredictable renewable energy supply and with co-generation
(that needs to consider both electricity and heat demand). In this paper, we
study online algorithms for the microgrid generation scheduling problem with
intermittent renewable energy sources and co-generation, with the goal of
maximizing the cost-savings with local generation. Based on the insights from
the structure of the offline optimal solution, we propose a class of
competitive online algorithms, called CHASE (Competitive Heuristic Algorithm
for Scheduling Energy-generation), that track the offline optimal in an online
fashion. Under typical settings, we show that CHASE achieves the best
competitive ratio among all deterministic online algorithms, and the ratio is
no larger than a small constant 3.Comment: 26 pages, 13 figures. It will appear in Proc. of ACM SIGMETRICS, 201
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