This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accoun...
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This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accounting for the inherent uncertainty affecting the main problem parameters (i.e. generation from renewable energy sources and demands). The problem provides the prosumer with the optimal scheduling of the shiftable loads and operations of the available storage systems that minimizes the expected overall electricity cost. Preliminary results, collected on three different categories of residential prosumers, have shown the effectiveness of the proposed approach in terms of cost saving. (C) 2019 Published by Elsevier Ltd.
The deregulation of electricity markets has driven the need to optimise market bidding strategies, e.g. when and how much electricity to buy or sell, in order to gain an economic advantage in a competitive market envi...
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The deregulation of electricity markets has driven the need to optimise market bidding strategies, e.g. when and how much electricity to buy or sell, in order to gain an economic advantage in a competitive market environment. The present work aims to determine optimal day-ahead market bidding curves for a microgrid comprised of a battery, power generator, photovoltaic (PV) system and an electricity load from a commercial building. Existing day-ahead market bidding models heuristically fix price values for each allowed bidding curve point prior to the optimisation problem or relax limitations set by market rules on the number of price-quantity points per curve. In contrast, this work integrates the optimal selection of prices for the construction of day-ahead market bidding curves into the optimisation of the energy system schedule;aiming to further enhance the bidding curve accuracy while remaining feasible under present market rules. The examined optimisation problem is formulated as a mixed integer linear programming (MILP) model, embedded in a two-stage stochastic programming approach. Uncertainty is considered in the electricity price and the PV power. First stage decisions are day-ahead market bidding curves, while the overall objective is to minimise the expected operational cost of the microgrid. The bidding strategy derived is then examined through Monte Carlo simulations by comparing it against a deterministic approach and two alternative stochastic bidding approaches from literature.
We propose a new method for certain multistage stochastic programs with linear or nonlinear objective function, combining a primal interior point approach with a linear-quadratic control problem over the scenario tree...
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We propose a new method for certain multistage stochastic programs with linear or nonlinear objective function, combining a primal interior point approach with a linear-quadratic control problem over the scenario tree. The latter problem, which is the direction finding problem for the barrier subproblem is solved through dynamic programming using Riccati equations. In this way we combine the low iteration count of interior point methods with an efficient solver for the subproblems. The computational results are promising, We have solved a financial problem with 1,000,000 scenarios, 15,777,740 variables and 16,888,850 constraints in 20 hours on a moderate computer. (C) 2002 Elsevier Science B.V. All rights reserved.
In [Euro. J. Operat. Res. 143 (2002) 452;Opt. Meth. Software 17 (2002) 383] a Riccati-based primal interior point method for multistage stochastic programmes was developed. This algorithm has several interesting featu...
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In [Euro. J. Operat. Res. 143 (2002) 452;Opt. Meth. Software 17 (2002) 383] a Riccati-based primal interior point method for multistage stochastic programmes was developed. This algorithm has several interesting features. It can solve problems with a nonlinear node-sepatable convex objective, local linear constraints and global linear constraints. This paper demonstrates that the algorithm can be efficiently parallelized. The solution procedure in the algorithm allows for a simple but efficient method to distribute the computations. The parallel algorithm has been implemented on a low-budget parallel computer, where we experience almost perfect linear speedup and very good scalability of the algorithm. (C) 2003 Elsevier Science B.V. All rights reserved.
Problems of multi-stage decision under uncertainty are usually classified into “stochastic” and “adaptive” ones, depending on whether the decision maker does or does not know the relevant probability distribution....
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Problems of multi-stage decision under uncertainty are usually classified into “stochastic” and “adaptive” ones, depending on whether the decision maker does or does not know the relevant probability distribution. If the Bayesian approach is taken, then, in the adaptive case, the decision maker is assumed to know the prior distribution of certain parameters. It is shown in the paper that the adaptive case is then reducible to the stochastic one.
Energy storage units offer vital balancing power for energy systems with an increasing amount of variable renewable energy (VRE) sources. The operation of such systems can be optimized by stochastic programming, which...
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Energy storage units offer vital balancing power for energy systems with an increasing amount of variable renewable energy (VRE) sources. The operation of such systems can be optimized by stochastic programming, which anticipates the uncertainty related to the variable renewable energy sources. However, these optimization problems can only be formulated for optimization horizons with a finite length, due to the rapidly increasing problem size and uncertainty in VRE production. Realistic valuation of the stored energy at the end of a horizon is important for long-term operation of the system. In this work, we investigate two different valuation methods, which are based on forecasted electricity prices, for storage-only and producer-oriented energy systems that participate in the day-ahead market. On a case study on the German day-ahead market, the methods yield competitive profits with reduced cycling of the energy storage unit and deviations with respect to the day-ahead trading.
From the point of view of revenue management, a bi-level optimization model is proposed to determine the seat allocation and discriminatory pricing for high speed rail. The relation between ticket prices and quantitie...
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ISBN:
(数字)9780784479810
ISBN:
(纸本)9780784479810
From the point of view of revenue management, a bi-level optimization model is proposed to determine the seat allocation and discriminatory pricing for high speed rail. The relation between ticket prices and quantities can be represented using demand functions. stochastic passenger demands and demand functions of high speed rail are integrated into this model. The objective is to maximize the expected total revenue. Discriminatory pricing principles and seat capacity constraints are considered simultaneously. For different market segments, discriminative ticket prices are determined in accordance with the given seat amount. The upper-level model is formulated as a nonlinear mathematical program. The lower-level model is formulated as a two-stage stochastic programming model.
When solving a decision problem under uncertainty via stochastic programming it is essential to choose or to build a suitable stochastic programming model taking into account the nature of the real-life problem, chara...
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When solving a decision problem under uncertainty via stochastic programming it is essential to choose or to build a suitable stochastic programming model taking into account the nature of the real-life problem, character of input data, availability of software and computer technology. Besides a brief review of history and achievements of stochastic programming, selected modeling issues concerning applications of multistage stochastic programs with recourse (the choice of the horizon, stages, methods for generating scenario trees, etc.) will be discussed. (C) 2002 Published by Elsevier Science B.V.
In 5G / beyond, local communication systems with small cells managed by micro operators are collecting attentions, where interference mitigation and power saving is considered as an important topic. This paper present...
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ISBN:
(纸本)9781665483032
In 5G / beyond, local communication systems with small cells managed by micro operators are collecting attentions, where interference mitigation and power saving is considered as an important topic. This paper presents an interference management method aided by spectrum database based on stochastic programming assuming non-line of sight (NLOS) environment. Regarding analysis of signal to interference plus noise ratio (SINR) under multiuser Rayleigh channel, density functions of SINR are approximated by exponential distributions. Then chance constraints in stochastic programming concerning outage SINR which are hard to deal with are converted to Monte Carlo expression for approximating predetermined outage SINR inequalities to improve the feasibility of the problem. Computer simulations show the proposed approach utilizing spectrum database is effective for performance improvement in small cellular system by micro operator.
We consider the case when part of the uncertainty faced by a decision maker is derived from actions of another independent actor who pursues her own aims. Each party sets its decisions in the next time period in respo...
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We consider the case when part of the uncertainty faced by a decision maker is derived from actions of another independent actor who pursues her own aims. Each party sets its decisions in the next time period in response to the other party's policy. We model this situation by introducing some ideas from game theory, but unlike this theory we do not focus on equilibrium and related optimality notions. Instead, we follow the framework of stochastic programming and take the view of one of the decision makers. Our model is placed in a telecommunication environment with a network owner and operators without their own network facilities. We give an extension to a multiperiod model.
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