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David Steen - One of the best experts on this subject based on the ideXlab platform.

  • Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models
    Applied Energy, 2015
    Co-Authors: David Steen, Markus Groissböck, Michael Stadler, Gonçalo Cardoso, Nicholas Deforest, Chris Marnay
    Abstract:

    Thermal Energy storage (TES) and Distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce Energy costs and decrease CO2 emissions from buildings by shifting Energy consumption to times with less emissions and/or lower Energy prices. To determine the feasibility of investing in TES in combination with other Distributed Energy Resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the Energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

  • Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models - eScholarship
    Applied Energy, 2014
    Co-Authors: David Steen
    Abstract:

    Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models I David Steen a,b , Michael Stadler a,c , Goncalo Cardoso a,d , Markus Groissbock a,c , Nicholas DeForest a , Chris Marnay a David Steen is an affiliate with Berkeley Lab, USA, and is with Chalmers University of Technology, Sweden Michael Stadler is with Berkeley Lab, USA, and leads the microgrid team at Berkeley Lab as well as the Center for Energy and Innovative Technologies, Austria Goncalo Cardoso is with Berkeley Lab, USA, and with Instituto Superior Tecnico - University of Lisbon, Portugal Nicholas DeForest is with Berkeley Lab, USA Markus Groissbock was an affiliate with Berkeley Lab, USA, and with the Center for Energy and Innovative Technologies, Austria Chris Marnay is a retired Staff Scientist and current affiliate with Berkeley Lab, and is President of Microgrid Design of Mendocino, USA a Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 90R1121 Berkeley CA 94720 USA b Chalmers University of Technology Department of Energy and Environment SE-412 96 Goteborg, Sweden Phone: +46(0)31-772 16 63 c Center for Energy and innovative Technologies - CET Doberggasse 9 A-3681 Hofamt Priel Austria d Instituto Superior Tecnico - University of Lisbon Avenida Rovisco Pais 1, 1049-001 Lisboa Portugal Corresponding author e-mail: david.steen@chalmers.se, mobile: 0046-739169596 Second contact: MStadler@lbl.gov DER-CAM has been funded partly by the Office of Electricity Delivery and Energy Reliability, Distributed Energy Program of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The Distributed Energy Resources Customer Adoption Model (DER-CAM) has been designed at Lawrence Berkeley National Laboratory (LBNL). Furthermore, Chalmers Energy Initiative is greatly acknowledged for funding D. Steen’s guest research visit to Lawrence Berkeley National Laboratory. July 2014

Yunhan Xiao - One of the best experts on this subject based on the ideXlab platform.

  • Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties
    Energies, 2020
    Co-Authors: Shijie Zhang, Yunhan Xiao
    Abstract:

    Distributed Energy Resource (DER) systems have elicited increasing attention and applications because of their excellent economic and environmental performance. However, uncertainties exist in such systems, preventing their potential advantages to be realized. In this study, an interval optimization-based model for the optimal design of DER systems is proposed, considering uncertainties in Energy prices, renewable Energy intensity, and load demands. Uncertainties are described as interval numbers, and the uncertain optimization model is transformed into a deterministic optimization problem using the order relationship and probability degree of interval numbers. The proposed model is applied to a typical hospital in Lianyungang, China, and its effectiveness is verified. One deterministic case and three uncertain cases are designed. The effects of uncertainties on system configuration and economic performance are also analyzed, and the optimal operation strategy under the three uncertainties is determined. A sensitivity analysis is conducted to analyze the effects of probability degree and weighting coefficient on total annual cost. Results show that uncertainties exert a cumulative effect on system optimization outcomes, and the proposed interval optimization model can obtain robust solutions to uncertainties.

  • Optimal design of Distributed Energy Resource systems based on two-stage stochastic programming
    Applied Thermal Engineering, 2017
    Co-Authors: Yun Yang, Shijie Zhang, Yunhan Xiao
    Abstract:

    Abstract Multiple uncertainties exist in the optimal design of Distributed Energy Resource (DER) systems. The expected Energy, economic, and environmental benefits may not be achieved and a deficit in Energy supply may occur if the uncertainties are not handled properly. This study focuses on the optimal design of DER systems with consideration of the uncertainties. A two-stage stochastic programming model is built in consideration of the discreteness of equipment capacities, equipment partial load operation and output bounds as well as of the influence of ambient temperature on gas turbine performance. The stochastic model is then transformed into its deterministic equivalent and solved. For an illustrative example, the model is applied to a hospital in Lianyungang, China. Comparative studies are performed to evaluate the effect of the uncertainties in load demands, Energy prices, and renewable Energy intensity separately and simultaneously on the system’s economy and optimal design. Results show that the uncertainties in load demands have a significant effect on the optimal system design, whereas the uncertainties in Energy prices and renewable Energy intensity have almost no effect. Results regarding economy show that it is obviously overestimated if the system is designed without considering the uncertainties.

  • An MILP (mixed integer linear programming) model for optimal design of district-scale Distributed Energy Resource systems
    Energy, 2015
    Co-Authors: Yun Yang, Shijie Zhang, Yunhan Xiao
    Abstract:

    This study focuses on the optimal design of district-scale DER (Distributed Energy Resource) systems in which Energy is produced outside Energy-consuming buildings and sent to the buildings through the Energy distribution networks. A MILP (mixed integer linear programming) model is constructed. The model can achieve simultaneous optimization of locations (i.e., site for Energy generation), synthesis (i.e., type, capacity, and number of equipment as well as structure of the Energy distribution networks), and operation strategies of the entire system. The model is built in consideration of discreteness of equipment capacities, equipment partial load operation and output bounds as well as the influence of ambient temperature on gas turbine performance. The objective function is the total annual cost for investing, maintaining, and operating the system. The model is applied to an urban area in Guangzhou (China), and its validity and effectiveness is verified. Results show that the adoption of the proposed DER system provides significant economic benefits in respect to the conventional Energy system.

  • Optimal design of Distributed Energy Resource systems coupled with Energy distribution networks
    Energy, 2015
    Co-Authors: Yun Yang, Shijie Zhang, Yunhan Xiao
    Abstract:

    Abstract This study focuses on the optimal design of DER (Distributed Energy Resource) systems coupled with heating, cooling, and power distribution networks. A superstructure-based MILP (mixed integer linear programming) model is constructed. The model can achieve simultaneous optimization of synthesis (i.e., type, capacity, number, and location of equipment as well as structure of the Energy distribution networks) and operation strategies of the entire system. The model is built in consideration of discreteness of equipment capacities, equipment partial load operation and output bounds as well as the influence of ambient temperature on gas turbine performance. The objective function is the annual total cost for investing, maintaining, and operating the system. To provide an illustrative example, the model is applied to an urban area in Guangzhou, China. Comparative studies are performed to quantify the impact of Distributed generation and the introduction of Energy distribution networks and/or storages. Results show that the adoption of the DER system provides significant economic benefits. The introduction of Energy distribution networks and/or storages has significant and similar effects on optimal system configuration and can improve the system's economic efficiency because of the elimination of some of the strong coupling relation between demands and generators.

Robert Broadwater - One of the best experts on this subject based on the ideXlab platform.

  • Maximizing Distributed Energy Resource Hosting Capacity of Power System in South Korea Using Integrated Feeder, Distribution, and Transmission System
    Energies, 2020
    Co-Authors: Victor Widiputra, Jaesung Jung, Junhyuk Kong, Yejin Yang, Robert Broadwater
    Abstract:

    Intermittent power generated from renewable Distributed Energy Resource (DER) can create voltage stability problems in the system during peak power production in the low demand period. Thus, the existing standard for operation and management of the distribution system limits the penetration level of the DER and the amount of load in a power system. In this standard, the hosting capacity of the DER is limited to each feeder at a level where the voltage problem does not occur. South Korea applied this standard, thereby making it hard to achieve its DER target. However, by analyzing the voltage stability of an integrated system, the hosting capacity of DER can be increased. Therefore, in this study, the maximum hosting capacity of DER is determined by analyzing an integrated transmission and distribution system. Moreover, the fast voltage stability index (FVSI) is used to verify the determined hosting capacity of DER. For this, the existing interconnection standard of DER at a feeder, distribution system, and transmission system level is investigated. Subsequently, a Monte Carlo simulation is performed to determine the maximum penetration of the DER at a feeder level, while varying the load according to the standard test system in South Korea. The actual load generation profile is used to simulate system conditions in order to determine the maximum DER hosting capacity.

  • Monte Carlo analysis of Plug-in Hybrid Vehicles and Distributed Energy Resource growth with residential Energy storage in Michigan
    Applied Energy, 2013
    Co-Authors: Jaesung Jung, Yongju Cho, Danling Cheng, Ahmet Onen, Reza Arghandeh, Murat Dilek, Robert Broadwater
    Abstract:

    This paper considers system effects due to the addition of Plug-in Hybrid Vehicles (PHEV) and Distributed Energy Resource (DER) generation. The DER and PHEV are considered with Energy storage technology applied to the residential distribution system load. Two future year scenarios are considered, 2020 and 2030. The models used are of real distribution circuits located near Detroit, Michigan, and every customer load on the circuit and type of customer are modeled. Monte Carlo simulations are used to randomly select customers that receive PHEV, DER, and/or storage systems. The Monte Carlo simulations provide not only the expected average result, but also its uncertainty. The adoption scenarios are investigated for both summer and winter loading conditions.

Shijie Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Optimal Design of Distributed Energy Resource Systems under Uncertainties Based on Two-Stage Robust Optimization
    Journal of Thermal Science, 2020
    Co-Authors: Shijie Zhang
    Abstract:

    Distributed Energy Resource (DER) systems are widely used owing to their excellent economic and environmental performance. However, uncertainties in the system generate difficulties in the optimal design of DER systems. In practice, the distribution of uncertain parameters is generally unknown. In this work, a two-stage robust optimization (RO) model was proposed for the optimal design of DER systems considering uncertainties in renewable Energy intensity, Energy prices, and load demands. Three uncertainty sets (i.e., the box, ellipsoid, and convex-hull uncertainty sets) were adopted to describe the distribution of uncertain parameters, and the proposed two-stage RO problem was solved using affine decision rules. A typical hospital in Lianyungang, Jiangsu Province, China, was selected as the case study object, and the effectiveness of the model was verified. The case study results showed that uncertainties in Energy prices and load demands have a significant impact on system configuration and economic performance, and mainly affect the installed capacities of gas boilers, absorption chillers, and storages. Uncertainty set will affect the optimization results and an appropriate uncertainty set should be adopted to describe uncertainties precisely and increase accuracy of results.

  • Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties
    Energies, 2020
    Co-Authors: Shijie Zhang, Yunhan Xiao
    Abstract:

    Distributed Energy Resource (DER) systems have elicited increasing attention and applications because of their excellent economic and environmental performance. However, uncertainties exist in such systems, preventing their potential advantages to be realized. In this study, an interval optimization-based model for the optimal design of DER systems is proposed, considering uncertainties in Energy prices, renewable Energy intensity, and load demands. Uncertainties are described as interval numbers, and the uncertain optimization model is transformed into a deterministic optimization problem using the order relationship and probability degree of interval numbers. The proposed model is applied to a typical hospital in Lianyungang, China, and its effectiveness is verified. One deterministic case and three uncertain cases are designed. The effects of uncertainties on system configuration and economic performance are also analyzed, and the optimal operation strategy under the three uncertainties is determined. A sensitivity analysis is conducted to analyze the effects of probability degree and weighting coefficient on total annual cost. Results show that uncertainties exert a cumulative effect on system optimization outcomes, and the proposed interval optimization model can obtain robust solutions to uncertainties.

  • Optimal design of Distributed Energy Resource systems based on two-stage stochastic programming
    Applied Thermal Engineering, 2017
    Co-Authors: Yun Yang, Shijie Zhang, Yunhan Xiao
    Abstract:

    Abstract Multiple uncertainties exist in the optimal design of Distributed Energy Resource (DER) systems. The expected Energy, economic, and environmental benefits may not be achieved and a deficit in Energy supply may occur if the uncertainties are not handled properly. This study focuses on the optimal design of DER systems with consideration of the uncertainties. A two-stage stochastic programming model is built in consideration of the discreteness of equipment capacities, equipment partial load operation and output bounds as well as of the influence of ambient temperature on gas turbine performance. The stochastic model is then transformed into its deterministic equivalent and solved. For an illustrative example, the model is applied to a hospital in Lianyungang, China. Comparative studies are performed to evaluate the effect of the uncertainties in load demands, Energy prices, and renewable Energy intensity separately and simultaneously on the system’s economy and optimal design. Results show that the uncertainties in load demands have a significant effect on the optimal system design, whereas the uncertainties in Energy prices and renewable Energy intensity have almost no effect. Results regarding economy show that it is obviously overestimated if the system is designed without considering the uncertainties.

  • An MILP (mixed integer linear programming) model for optimal design of district-scale Distributed Energy Resource systems
    Energy, 2015
    Co-Authors: Yun Yang, Shijie Zhang, Yunhan Xiao
    Abstract:

    This study focuses on the optimal design of district-scale DER (Distributed Energy Resource) systems in which Energy is produced outside Energy-consuming buildings and sent to the buildings through the Energy distribution networks. A MILP (mixed integer linear programming) model is constructed. The model can achieve simultaneous optimization of locations (i.e., site for Energy generation), synthesis (i.e., type, capacity, and number of equipment as well as structure of the Energy distribution networks), and operation strategies of the entire system. The model is built in consideration of discreteness of equipment capacities, equipment partial load operation and output bounds as well as the influence of ambient temperature on gas turbine performance. The objective function is the total annual cost for investing, maintaining, and operating the system. The model is applied to an urban area in Guangzhou (China), and its validity and effectiveness is verified. Results show that the adoption of the proposed DER system provides significant economic benefits in respect to the conventional Energy system.

  • Optimal design of Distributed Energy Resource systems coupled with Energy distribution networks
    Energy, 2015
    Co-Authors: Yun Yang, Shijie Zhang, Yunhan Xiao
    Abstract:

    Abstract This study focuses on the optimal design of DER (Distributed Energy Resource) systems coupled with heating, cooling, and power distribution networks. A superstructure-based MILP (mixed integer linear programming) model is constructed. The model can achieve simultaneous optimization of synthesis (i.e., type, capacity, number, and location of equipment as well as structure of the Energy distribution networks) and operation strategies of the entire system. The model is built in consideration of discreteness of equipment capacities, equipment partial load operation and output bounds as well as the influence of ambient temperature on gas turbine performance. The objective function is the annual total cost for investing, maintaining, and operating the system. To provide an illustrative example, the model is applied to an urban area in Guangzhou, China. Comparative studies are performed to quantify the impact of Distributed generation and the introduction of Energy distribution networks and/or storages. Results show that the adoption of the DER system provides significant economic benefits. The introduction of Energy distribution networks and/or storages has significant and similar effects on optimal system configuration and can improve the system's economic efficiency because of the elimination of some of the strong coupling relation between demands and generators.

Sung-kwan Joo - One of the best experts on this subject based on the ideXlab platform.

  • Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants
    Energies, 2019
    Co-Authors: Sung-kwan Joo
    Abstract:

    Virtual power plants (VPPs) have been widely researched to handle the unpredictability and variable nature of renewable Energy sources. The Distributed Energy Resources are aggregated to form into a virtual power plant and operate as a single generator from the perspective of a system operator. Power system operators often utilize the incentives to operate virtual power plants in desired ways. To maximize the revenue of virtual power plant operators, including its incentives, an optimal portfolio needs to be identified, because each renewable Energy source has a different generation pattern. This study proposes a stochastic mixed-integer programming based Distributed Energy Resource allocation method. The proposed method attempts to maximize the revenue of VPP operators considering market incentives. Furthermore, the uncertainty in the generation pattern of renewable Energy sources is considered by the stochastic approach. Numerical results show the effectiveness of the proposed method.