This paper proposes a new method for the multi-objective sizing of microgrids, which aims to minimize both the investment and operation costs, as well as the carbon footprint of their components and energy usage. The ...
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This paper proposes a new method for the multi-objective sizing of microgrids, which aims to minimize both the investment and operation costs, as well as the carbon footprint of their components and energy usage. The method employs mixedintegerlinearprogramming (MILP) and Pareto optimization to assess the balance between economic and environmental goals, constructed using the epsilon-constraint method. Additionally, the overall operation of a grid-connected microgrid is optimized considering unintentional islanding contingencies through a stochastic scenario-based mathematical programming model. Tests were conducted using data from CampusGrid, a real microgrid located at the University of Campinas (UNICAMP) in Brazil. The model determines the optimal size and type of Distributed Energy Resources (DERs), such as local Thermal Generation (TG), Photovoltaic (PV) systems, Battery Energy Storage Systems (BESSs), and load/generation curtailment requirements in islanded mode. For carbon-intensity comparison, a case study was conducted using attributes and parameters from the city of Beijing in China. The results provide valuable insights into the optimal sizing and configuration of microgrids, with an emphasis on cost-efficient and environmentally sounding energy solutions.
Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pr...
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Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pre-defined stochastic scenarios, without searching for the "best" scenarios and testing the optimization models with these scenarios. Moreover, little work both optimized RECs under uncertainty and distributed optimal life-cycle costs (investment and operation) among members. Thus, the objectives are: i) identifying the "best" set of stochastic scenarios of solar irradiance and user electricity demands and ii) assessing the accuracy of the "stochastic forecasts" of the total system costs and unit sizes, obtained by solving a stochastic programming model based on the "best" scenarios. The proposed novel procedure shifts the "present moment" back in time to split historical data into "past" and "future" periods used to identify the "best" scenarios and compare the "stochastic forecasts" with the utopic "perfect forecasts" based on the perfect knowledge of real data, respectively. The small errors between these forecasts in the optimal life-cycle costs (less than 2 %) and sizes (3-13 %) indicate good effectiveness of the suggested procedure. Also, the optimal life-cycle costs of "stochastic forecasts" are fairly distributed among users by applying the Shapley value mechanism.
The paper suggests a 3-steps methodology, integrated in a unique-procedure, to optimally design a hybrid RPP (Renewable Power Plant) to be installed in the available areas of railway's power plants. The procedure ...
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The paper suggests a 3-steps methodology, integrated in a unique-procedure, to optimally design a hybrid RPP (Renewable Power Plant) to be installed in the available areas of railway's power plants. The procedure takes into account power profiles and time schedule of trains, traction line's features, sizing and siting of TPSSs (Traction Power Substations) and features of renewable sources as PV (Photovoltaic) and wind turbines for optimizing the siting of the RPP, considering the investment convenience. The optimal sizing of a hybrid power plant is based on mixed-integer linear programming (MILP) taking into account the energy absorbed by TPSSs, the physical and geometric constraints and the operating and maintenance costs of RPP. The integrated procedure has been tested on a real case study regarding a new 3 kV DC railway located in the South of Italy. The main results show that a hybrid RPP with 4980 PV panels and 3 wind turbines installed in a TPSS area, adopting a capital cost of 645,000 /MW for PV panels and 3000 /kW for wind farms, ensures an annual revenue of 227,439 , with a total investment of 897,400 . Results show that the available areas in TPSS should be the target of relevant investments to support a new sustainable and green power traction supply system.
This paper studies the investment planning of a decarbonised Norwegian continental shelf energy system considering the connection and interfaces with the European energy system. A multihorizon stochastic mixed-integer...
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ISBN:
(纸本)9780791885956
This paper studies the investment planning of a decarbonised Norwegian continental shelf energy system considering the connection and interfaces with the European energy system. A multihorizon stochastic mixed-integer linear programming model is developed for such a problem. We consider short-term uncertainties, including wind and solar capacity factors, energy load, platform production profiles, and hydro power production limits. Hydrogen based energy hubs are considered both onshore and offshore for potential renewable power generation, distribution and storage. Future hydrogen market or demand is not included in the model. The results of multi-period planning towards 2050 show that: (a) offshore energy hubs are essentially wind power generation, conversion and distribution hubs, (b) a combination of offshore wind and power from shore may be a cost-efficient pathway for cutting emissions from the Norwegian continental shelf, (c) a total of 1.6 GW offshore wind may be needed to achieve a near zero emission Norwegian continental shelf energy system, 80% of which may be added in the first investment period and (d) offshore grid design is important for decarbonisation by distributing wind power efficiently;all five offshore platform clusters are connected to at least three other clusters by 2040, and they are fully connected by 2050.
In this study, we designed a vehicle routing problem model for chocolate manufacturer company to optimize their distribution system. Our model includes 3 warehouses, heterogenous fleet, multi period, and about two doz...
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ISBN:
(纸本)9783030904210;9783030904203
In this study, we designed a vehicle routing problem model for chocolate manufacturer company to optimize their distribution system. Our model includes 3 warehouses, heterogenous fleet, multi period, and about two dozen customers. We got our customer data directly from company. We considered distance between the customers, their demands and vehicle capacities. Sensitivity analysis was made to make sure that it is effective. In the model that we designed, we compared 10-15-20 customer cases with 3 depot, 1 depot, heterogeneous and homogeneous fleet scenarios. According to results of the study homogeneous fleet scenarios are costly. In addition, using single depot is costly than using multiple depots.
In manufacturing systems, aisles are paths which are used for the movement of workers, transportation devices, and materials. The aisle structure contributes to layout efficiency by reducing material handling costs, m...
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In manufacturing systems, aisles are paths which are used for the movement of workers, transportation devices, and materials. The aisle structure contributes to layout efficiency by reducing material handling costs, mean flow time and the amount of space needed, and providing smooth transportation. Therefore, to achieve a good layout, it is essential to determine the position of facilities such as machines and workstations, but also the corresponding aisle structure. In this article, we analyze the requirements for the design of an efficient aisle structure and propose a formulation of the corresponding layout problem as a mixed-integer linear programming model. This formulation allows the layout of unequal-area facilities and the aisle structure to be simultaneously optimized. In optimizing the aisle structure, issues such as optimizing the number, position, and width of the aisles, the position of the entrance and exit doors, and how to connect them to the aisles are studied. By optimizing the number and width of the aisles, the proposed approach contributes towards optimizing transportation traffic. A branch-and-cut algorithm, improved by adding optimality cuts and efficient branching and node strategies, is used to solve the problem. Finally, a set of computational experiments is performed to show the effectiveness of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.
We present a general numerical solution method for control problems with state variables defined by a linear PDE over a finite set of binary or continuous control variables. We show empirically that a naive approach t...
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We present a general numerical solution method for control problems with state variables defined by a linear PDE over a finite set of binary or continuous control variables. We show empirically that a naive approach that applies a numerical discretization scheme to the PDEs to derive constraints for a mixed-integerlinear program (MILP) leads to systems that are too large to be solved with state-of-the-art solvers for MILPs, especially if we desire an accurate approximation of the state variables. Our framework comprises two techniques to mitigate the rise of computation times with increasing discretization level: First, the linear system is solved for a basis of the control space in a preprocessing step. Second, certain constraints are just imposed on demand via the IBM ILOG CPLEX feature of a lazy constraint callback. These techniques are compared with an approach where the relations obtained by the discretization of the continuous constraints are directly included in the MILP. We demonstrate our approach on two examples: modeling of the spread of wildfire and the mitigation of water contamination. In both examples the computational results demonstrate that the solution time is significantly reduced by our methods. In particular, the dependence of the computation time on the size of the spatial discretization of the PDE is significantly reduced.
In this paper, we introduce a mixed-integerlinear program for a shift scheduling problem in a German potash mine. In particular, we consider a short-term (work shift) production scheduling problem, where drill-and-bl...
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In this paper, we introduce a mixed-integerlinear program for a shift scheduling problem in a German potash mine. In particular, we consider a short-term (work shift) production scheduling problem, where drill-and-blast mining operations have to be assigned to machines and workers simultaneously. Since we deal with several sequence-dependent setup, changeover, and removal times, TSP-variables are used in the mathematical program to determine the processing-sequence of the operations on each worker and each machine, respectively. In addition, several mining-specific requirements are taken into account to obtain a solution that can be put into practice. Computational experiments are conducted on problem instances of realistic size derived from real-world data. The results show that our new mixed-integerlinear formulation outperforms both existing solution procedures for the problem at hand. (C) 2020 Elsevier B.V. All rights reserved.
This letter formulates network connectivity as Miller-Tucker-Zemlin (MTZ) constraints and incorporates them into the mixed-integer linear programming (MILP) model for the optimal transmission switching (OTS) problem. ...
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This letter formulates network connectivity as Miller-Tucker-Zemlin (MTZ) constraints and incorporates them into the mixed-integer linear programming (MILP) model for the optimal transmission switching (OTS) problem. The connectivity constraints are linear and for a power network with n buses, m branches, and d loads in pre-contingency or each post-contingency state there are approximately O(n + 5m + d) constraints, and O(n) continuous and O(2 m) binary variables, which is much smaller than those in the existing formulations. The MILP OTS model with the proposed connectivity constraints can be readily solved by well-developed MILP solvers. Case studies on the PJM 5-bus system, IEEE 300-bus system, and French 1888-bus system validate the effectiveness of the proposed model.
The integration of renewable energy sources, particularly solar photovoltaics, into household power supply has become increasingly popular due to its potential to reduce energy costs and environmental impact. However,...
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The integration of renewable energy sources, particularly solar photovoltaics, into household power supply has become increasingly popular due to its potential to reduce energy costs and environmental impact. However, solar power variability and new regulative changes concerning excess solar energy compensation schemes call for effective energy storage management and sizing to ensure a stable and profitable electricity supply. This paper focuses on optimizing residential battery storage systems under different electricity pricing schemes such as time-of-use tariffs, dynamic pricing, and different excess solar energy compensation schemes. The central question addressed is how different pricing mechanisms and compensation strategies for excess solar energy, as well as varying battery storage investment costs, determine the optimal sizing of battery storage systems. A comprehensive mixed-integer linear programming model is developed to analyze these factors, incorporating various financial and operational parameters. The model is applied to a residential case study in Croatia, examining the impact of monthly net metering/billing, 15 min net billing, and dynamic pricing on optimal battery storage sizing and economic viability.
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