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 They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand).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.
For large power systems, UC involves scheduling of a large number gigantic power plants of several hundred if not thousands of megawatts with heterogeneous operating constraints and logistics behind each action @cite_35 . The problem is very challenging to solve and has been shown to be NP-Complete in general We note that @math in (3a)-(3d) is an instance of UC, and that UC is NP-hard in general does not imply that the instance @math is also NP-hard. @cite_13 . Sophisticated approaches proposed in the literature for solving UC include mixed integer programming @cite_29 , dynamic programming @cite_31 , and stochastic programming @cite_41 . There have also been investigations on UC with high renewable energy penetration @cite_20 , based on over-provisioning approach. After UC determines the on off status of generators, ED computes their output levels by solving a nonlinear optimization problem using various heuristics without altering the on off status of generators @cite_23 . There is also recent interest in involving CHP generators in ED to satisfy both electricity and heat demand simultaneously @cite_12 . See comprehensive surveys on UC in @cite_35 and on ED in @cite_23 .
@cite_35 @cite_41 @cite_29 @cite_23 @cite_31 @cite_13 @cite_12 @cite_20
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This paper describes a Benders decomposition-based framework for solving the large scale energy management problem that was posed for the ROADEF 2010 challenge. The problem was taken from the power industry and entailed scheduling the outage dates for a set of nuclear power plants, which need to be regularly taken down for refueling and maintenance, in such a way that the expected cost of meeting the power demand in a number of potential scenarios is minimized. We show that the problem structure naturally lends itself to Benders decomposition; however, not all constraints can be included in the mixed integer programming model. We present a two phase approach that first uses Benders decomposition to solve the linear programming relaxation of a relaxed version of the problem. In the second phase, integer solutions are enumerated and a procedure is applied to make them satisfy constraints not included in the relaxed problem. To cope with the size of the formulations arising in our approach we describe efficient preprocessing techniques to reduce the problem size and show how aggregation can be applied to each of the subproblems. Computational results on the test instances show that the procedure competes well on small instances of the problem, but runs into difficulty on larger ones. Unlike heuristic approaches, however, this methodology can be used to provide lower bounds on solution quality. This paper presents a security-constrained unit commitment (SCUC) algorithm which takes into account the intermittency and volatility of wind power generation. The UC problem is solved in the master problem with the forecasted intermittent wind power generation. Next, possible scenarios are simulated for representing the wind power volatility. The initial dispatch is checked in the subproblem and generation redispatch is considered for satisfying the hourly volatility of wind power in simulated scenarios. If the redispatch fails to mitigate violations, Benders cuts are created and added to the master problem to revise the commitment solution. The iterative process between the commitment problem and the feasibility check subproblem will continue until simulated wind power scenarios can be accommodated by redispatch. Numerical simulations indicate the effectiveness of the proposed SCUC algorithm for managing the security of power system operation by taking into account the intermittency and volatility of wind power generation. In modern world, every industries including the power industry address technological changes. Therefore, it is necessary to keep track of international experiences and activities taking place in every field. This paper provides an overview of the concept of Unit Commitment (UC) problem with a bibliographical survey of relevant background, the present state and potential methodologies used for solving the concern problem. From the literature, UC problem can be categorized based on the system characteristics and policies applied. This paper reviews not only on regulated and deregulated power systems but also on power systems with renewable energy sources and storage system. In addition, it presents a comprehensive review of the methodologies, which covers a wide span of deterministic, meta-heuristic and hybrid approaches including multi-agent system approach. In terms of contribution, it formulates the problem clearly and describes appropriate approaches to solve the problem. The collected literature of methodologies has been divided into many sections, so that new researchers do not face any difficulty in carrying out research on UC problem under both the regulated and deregulated power industries as well as power system with renewable energy sources and storage system for next generation. We study the problem of non-preemptive scheduling to minimize energy consumption for devices that allow dynamic voltage scaling. Specifically, consider a device that can process jobs in a non-preemptive manner. The input consists of (i) the set R of available speeds of the device, (ii) a set J of jobs, and (iii) a precedence constraint Π among J. Each job j in J, defined by its arrival time aj, deadline dj, and amount of computation cj, is supposed to be processed by the device at a speed in R. Under the assumption that a higher speed means higher energy consumption, the power-saving scheduling problem is to compute a feasible schedule with speed assignment for the jobs in J such that the required energy consumption is minimized. This paper focuses on the setting of weakly dynamic voltage scaling, i.e., speed change is not allowed in the middle of processing a job. To demonstrate that this restriction on many portable power-aware devices introduces hardness to the power-saving scheduling problem, we prove that the problem is NP-hard even if aj = aj ′ and dj = dj ′ hold for all j,j ′∈ Jand |R|=2. If |R|<∞, we also give fully polynomial-time approximation schemes for two cases of the general NP-hard problem: (a) all jobs share a common arrival time, and (b) Π = ∅ and for any j,j ′ ∈ J, aj ≤ aj ′ implies dj ≤ dj ′. To the best of our knowledge, there is no previously known approximation algorithm for any special case of the NP-hard problem.