A Dispatch Optimization Model for Hybrid Renewable and Battery Systems Incorporating a Battery Degradation Model

2021 ◽  
pp. 1-13
Author(s):  
Sahana Upadhya ◽  
Michael J. Wagner
Keyword(s):  
Optimization Model ◽  
Time Horizon ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Global Optimality ◽  
Battery Capacity ◽  
Battery Systems ◽  
Battery Degradation ◽  
Number Of Cycles

Abstract A recent increase in the integration of renewable energy systems in existing power grids along with a lack of integrated dispatch models has led to waste in power produced. This paper presents a mixed-integer nonlinear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Loss in capacity is penalized in the objective function of the optimization model, thereby disincentivizing high and low SOCs and frequent cycling. A rolling time horizon optimization approach is used to overcome the computational difficulties of achieving global optimality within a long-term time horizon. By incorporating battery degradation, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model within a case study using a sample photovoltaic system generation time series that considers multiple battery capacities. The results indicate that the optimal battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between battery operational decisions and the resultant capacity fade.

A Dispatch Optimization Model for Hybrid Renewable and Battery Systems Incorporating a Battery Degradation Model

2021 ◽  
Author(s):  
Sahana Upadhya ◽  
Michael J. Wagner
Keyword(s):  
Optimization Model ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Global Optimality ◽  
Operational Parameters ◽  
Time Model ◽  
Battery Capacity ◽  
Battery Systems ◽  
Battery Degradation ◽  
Number Of Cycles

Abstract Recently, there has been an increased level of integration of renewable energy systems in existing power grids. Lack of integrated dispatch models has led to waste in power produced. This paper proposes a mixed-integer linear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Finally, the loss in capacity is penalized in the objective function of the optimization model, thereby indirectly penalizing high and low SOCs and frequent cycling. To overcome the computational difficulties of achieving global optimality, a rolling time horizon optimization approach is used. By incorporating battery degradation in the real-time model, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model by assessing sample generator time series profiles with a range of battery capacities. The results demonstrate that the battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between operational parameters of the battery and the capacity fade.

Velocity Optimization and Robust Energy Management of Connected Power-Split Hybrid Electric Vehicles

2021 ◽  
Author(s):  
Seyedeh Mahsa Sotoudeh ◽  
Baisravan HomChaudhuri
Keyword(s):  
Energy Management ◽  
Time Horizon ◽  
Control Method ◽  
Optimization Approach ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Short Term ◽  
Power Split ◽  
Energy Manager

Abstract This paper focuses on an eco-driving based hierarchical robust energy management strategy for connected automated HEVs in the presence of uncertainty. The proposed control strategy includes a velocity optimizer, which evaluates the optimal vehicle velocity, and a powertrain energy manager, which evaluates the optimal power split between the engine and the battery in a hierarchical framework. The velocity optimizer accounts for regenerative braking and minimizes the total driving power and friction braking over a short control horizon. The hierarchical powertrain energy manager employs a long- and short-term strategy where it first approximately solves its problem over a long time horizon (the whole trip time in this paper) using the traffic data obtained from vehicle-to-infrastructure (V2I) connectivity. This is followed by a short-term decision maker that utilizes the velocity optimizer and long-term solution, and solves the energy management problem over a relatively short time horizon using robust prediction control methods to factor in any uncertainty in the velocity profile due to uncertain traffic. We solve the long-term energy management problem using pseudospectral optimal control method, and the short-term problem using robust tube-based model predictive control(MPC) method. Simulation results show the competence of our proposed approach, where our proposed co-optimization approach with long- and short-term solution results in ≈ 12% more energy efficiency than a baseline co-optimization approach.

scholarly journals A scenario-based optimization model for planning and redesigning the sale and after-sales services closed-loop supply chain

2020 ◽  
Author(s):  
Nazanin Esmaeili ◽  
Ebrahim Teimoury ◽  
Fahimeh Pourmohammadi
Keyword(s):  
Supply Chain ◽  
Customer Satisfaction ◽  
Optimization Model ◽  
Linear Optimization ◽  
Spare Parts ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Warranty Period ◽  
Linear Optimization Model ◽  
Mixed Integer Linear Optimization

In today's competitive world, the quality of after-sales services plays a significant role in customer satisfaction and customer retention. Some after-sales activities require spare parts and owing to the importance of customer satisfaction, the needed spare parts must be supplied until the end of the warranty period. In this study, a mixed-integer linear optimization model is presented to redesign and plan the sale and after-sales services supply chain that addresses the challenges of supplying spare parts after the production is stopped due to demand reduction. Three different options are considered for supplying spare parts, including production/procurement of extra parts while the product is being produced, remanufacturing, and procurement of parts just in time they are needed. Considering the challenges of supplying spare parts for after-sales services based on the product's life cycle is one contribution of this paper. Also, this paper addresses the uncertainties associated with different parameters through Mulvey's scenario-based optimization approach. Applicability of the model is investigated using a numerical example from the literature. The results indicate that the production/procurement of extra parts and remanufacturing are preferred to the third option. Moreover, remanufacturing is recommended when the remanufacturing cost is less than 23% of the production cost.

scholarly journals Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 999 ◽  
Author(s):  
Holger Hesse ◽  
Volkan Kumtepeli ◽  
Michael Schimpe ◽  
Jorn Reniers ◽  
David Howey ◽  
...  
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Time Variability ◽  
Data Set ◽  
Electricity Cost ◽  
Battery Capacity ◽  
Battery Degradation ◽  
Installed Capacity

To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

scholarly journals Optimization Model for the Long-Term Operation of an Interprovincial Hydropower Plant Incorporating Peak Shaving Demands

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4804
Author(s):  
Rui Cao ◽  
Jianjian Shen ◽  
Chuntian Cheng ◽  
Jian Wang
Keyword(s):  
Power Generation ◽  
Optimization Model ◽  
Hydropower Plant ◽  
Mixed Integer ◽  
Short Term ◽  
Long Distance ◽  
Peak Shaving ◽  
Term Operation ◽  
Proposed Model

The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.

Optimal battery capacity of grid-connected PV-battery systems considering battery degradation

2022 ◽  
Vol 181 ◽  
pp. 10-23
Author(s):  
Yaling Wu ◽  
Zhongbing Liu ◽  
Jiangyang Liu ◽  
Hui Xiao ◽  
Ruimiao Liu ◽  
...  
Keyword(s):  
Battery Capacity ◽  
Battery Systems ◽  
Battery Degradation

Long-Term Multi-Objective Optimization Dispatch and its Evaluation in Wind Integrated Power Systems

2013 ◽  
Vol 732-733 ◽  
pp. 1033-1037 ◽  
Author(s):  
Ke Wang ◽  
Da Hai You ◽  
Kai Pan
Keyword(s):  
Optimization Model ◽  
Evaluation System ◽  
Evaluation Method ◽  
Electric Power Industry ◽  
Wind Farms ◽  
Fuzzy Comprehensive Evaluation ◽  
Mixed Integer ◽  
Piecewise Linearization ◽  
Multi Objective

With energy saving and emission reduction paid more and more attention in electric power industry, long-term optimization dispatch in wind-integrated power system must take into consideration many inconsistent factors. In this paper on the basis of comprehensive consideration of coal consumption, emissions, openness and impartiality, a long-term multi-objective fuzzy optimization dispatch model is established. The model can optimize jointly generation maintenance scheduling, unit commitment and power output. After piecewise linearization of the objectives and the constraints, the optimization model is converted to a mixed integer linear programming (MILP) problem. An efficient optimization software CPLEX is employed to solve the problem. In order to analyze and compare different generation scheduling, a series of indices and fuzzy comprehensive evaluation method are utilized to evaluate the optimal results obtained. The proposed model is tested in IEEE 118-bus system incorporating three wind farms. The results show that the optimization model and the evaluation system built are effective and feasible.

Strategien zur Minimierung von Opportunitätskosten der Totholzbereitstellung

2018 ◽  
Vol 169 (1) ◽  
pp. 9-17
Author(s):  
Fabian H. Härtl ◽  
Peter Langhammer ◽  
Thomas Knoke
Keyword(s):  
Ecosystem Services ◽  
Time Horizon ◽  
Economic Planning ◽  
Optimization Approach ◽  
Optimal Solutions ◽  
Private Forest ◽  
Hardwood Trees ◽  
Short Time ◽  
The Given

Strategies aimed to minimize opportunity costs regarding the provision of deadwood Besides the conventional use of wood for construction and heating, further ecosystem services of forests gain more and more importance, such as environmental protection and social welfare. Due to its long-term production, forest management is faced with additional biophysical and economic risks when providing these services. This article assesses how ecosystem services can be included in an economic planning model using as an example the provision of deadwood in commercial forests. For a private forest enterprise in Bavaria (Germany), different deadwood targets were combined with different grading variants and included in the optimization approach as side conditions. By comparing the different optimal solutions for different deadwood targets it was possible to derive the costs of an optimal strategy. The results indicate that costs widely vary not only depending on the amount, but also on the grading types of deadwood (entire tree, strong timber, branches), the tree species (softwood, hardwood), and the given time horizon to reach the deadwood target. As a conclusion for practitioners we derive that ecological and economic objectives can be combined in an optimal way by the examined enterprise if deadwood is provided from whole hardwood trees and if the goal does not have to be reached in a short time.

scholarly journals Optimization model for the design of distributed wastewater treatment networks

2012 ◽  
Vol 66 (2) ◽  
pp. 263-273 ◽  
Author(s):  
Nidret Ibric ◽  
Elvis Ahmetovic ◽  
Midhat Suljkanovic
Keyword(s):  
Wastewater Treatment ◽  
Nonlinear Programming ◽  
Optimization Model ◽  
Mixed Integer ◽  
Programming Approach ◽  
Global Optimality ◽  
Total Treatment ◽  
Minlp Problem ◽  
Mathematical Programming Approach ◽  
Water Recirculation

In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP) problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP) problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal). The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.

scholarly journals Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 258
Author(s):  
Benjamin Daniel Blat Belmonte ◽  
Stephan Rinderknecht
Keyword(s):  
Critical Role ◽  
Cost Optimization ◽  
Optimization Method ◽  
Transportation Systems ◽  
Limiting Factors ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Charging Infrastructure ◽  
Term Operation

As the electrification of the transportation sector advances, fleet operators have to rethink their approach regarding fleet management against the background of limiting factors, such as a reduced range or extended recharging times. Charging infrastructure plays a critical role, and it is worthwhile to consider its planning as an integral part for the long-term operation of an electric vehicle fleet. In the category of fixed route transportation systems, the predictable character of the routes can be exploited when planning charging infrastructure. After a prior categorization of stakeholders and their respective optimization objectives in the sector coupling domain, a cost optimization framework for fixed route transportation systems is presented as the main contribution of this work. We confirm previous literature in that there is no one-fits-all optimization method for this kind of problem. The method is tested on seven scenarios for the public transport operator of Darmstadt, Germany. The core optimization is formulated as a mixed integer linear programming (MILP) problem. All scenarios are terminated by the criterion of a maximum solving time of 48 h and provide feasible solutions with a relative MIP-gap between 7 and 24%.

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