Under the background of low-carbon and efficient development of energy system, renewable energy will be vigorously developed and gradually become the main force of energy system. However, renewable energy has great un...
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Under the background of low-carbon and efficient development of energy system, renewable energy will be vigorously developed and gradually become the main force of energy system. However, renewable energy has great uncertainty and randomness due to its own formation characteristics. In order to solve this problem and reduce the carbon emission of regional integrated energy system (RIES), this paper first introduces the optimal structure of RIES considering shared energy storage system (SESS) and power-to-gas (P2G). Secondly, a ladder type reward-punishment carbon trading mechanism model is proposed, which considers the reward and punishment interval of carbon emissions. At the meantime, an improved robustoptimization over time (ROOT) method based on index contribution degree is proposed for dynamic problem considering uncertainty, based on the effect of each solution operator in the robust solution calculation process. Finally, a scheduling model with the lowest daily operation cost is established, and the effectiveness of the proposed low-carbon operation method is verified by comparison. Experimental results show that under the same dynamic test environment, the calculation accuracy of the improved ROOT method based on the contribution degree is increased by 3.56% on average, the calculation time is shortened by 2.93% on average. Meanwhile, the carbon emission can be reduced by 8.13% by adopting the carbon emission reduction measures, which ensures the system under low-carbon conditions.
robustoptimization over time can effectively solve the problem of frequent solution switching in dynamic environments. In order to improve the search performance of dynamic robust optimization algorithm, a dynamic ro...
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robustoptimization over time can effectively solve the problem of frequent solution switching in dynamic environments. In order to improve the search performance of dynamic robust optimization algorithm, a dynamicrobust particle swarm optimization algorithm based on hybrid strategy (HS-DRPSO) is proposed in this paper. Based on the particle swarm optimization, the HS-DRPSO combines differential evolution algorithm and brainstorms an optimization algorithm to improve the search ability. Moreover, a dynamic selection strategy is employed to realize the selection of different search methods in the proposed algorithm. Compared with the other two dynamic robust optimization algorithms on five dynamic standard test functions, the results show that the overall performance of the proposed algorithm is better than other comparison algorithms.
This paper considers a particular class of dynamic robust optimization problems, where a large number of decisions must be made in the first stage, which consequently fix the constraints and cost structure underlying ...
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This paper considers a particular class of dynamic robust optimization problems, where a large number of decisions must be made in the first stage, which consequently fix the constraints and cost structure underlying a one-dimensional, linear dynamical system. We seek to bridge two classical paradigms for solving such problems, namely, (1) dynamic programming (DP), and (2) policies parameterized in model uncertainties (also known as decision rules), obtained by solving tractable convex optimization problems. We show that if the uncertainty sets are integer sublattices of the unit hypercube, the DP value functions are convex and supermodular in the uncertain parameters, and a certain technical condition is satisfied, then decision rules that are affine in the uncertain parameters are optimal. We also derive conditions under which such rules can be obtained by optimizing simple (i.e., linear) objective functions over the uncertainty sets. Our results suggest new modeling paradigms for dynamic robust optimization, and our proofs, which bring together ideas from three areas of optimization typically studied separately-robustoptimization, combinatorial optimization (the theory of lattice programming and supermodularity), and global optimization (the theory of concave envelopes)-may be of independent interest. We exemplify our findings in a class of applications concerning the design of flexible production processes, where a retailer seeks to optimally compute a set of strategic decisions (before the start of a selling season), as well as in-season replenishment policies. We show that, when the costs incurred are jointly convex, replenishment policies that depend linearly on the realized demands are optimal. When the costs are also piecewise affine, all the optimal decisions can be found by solving a single linear program of small size (when all decisions are continuous) or a mixed-integer, linear program of the same size (when some strategic decisions are discrete).
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