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Economic benefits of solar photovoltaics and battery systems at customer side studied via screening curve method

IEEJ Transactions on Power and Energy 2021年第3期141卷 247-254页

作者： Hoshino, Hikaru Department of Electrical Materials and Engineering Graduate School of Engineering University of Hyogo 2167 Shosha Himeji672-2280 Japan

This paper develops a method of economic evaluation of solar photovoltaics (PV) and battery systems based on the framework of Screening Curve Method (SCM). While the SCM has been known as an intuitive model to estimate the least-cost expansion of generation capacity in a bulk power system, this paper applies it to the problem of optimal sizing of PV and battery systems at customer side. For this, we propose to alter the procedure of load slicing employed in the existing SCM and to construct load slices using PV generation profile. As a result, we develop a method of economic evaluation of PV and battery systems where the customer's electricity demand and PV generation profile are represented using time series of 24 hours. The accuracy of calculated results by the developed method is verified by comparing with the results obtained by a linear programming model. An intuitive graphical analysis enabled by the developed method is illustrated through an example with typical demand and PV generation data. © 2021 The Institute of Electrical Engineers of Japan.

关键词： linear programming

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Restoration of linear disturbances from oil-and-gas exploration in boreal landscapes: How can network models help?

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年 348卷 119036页

作者： Yemshanov, Denys Simpson, Mackenzie Liu, Ning Petty, Aaron Koch, Frank H. Neilson, Eric Chand, Cynthia Duffy, George Hoyles, Vita Mallon, Chris Nat Resources Canada Great Lakes Forestry Ctr Canadian Forest Serv Sault Ste Marie ON Canada Alberta Environm & Protected Areas Edmonton AB Canada USDA Forest Serv Southern Res Stn Eastern Forest Environm Threat Assessment Ctr Res Triangle Pk NC USA Nat Resources Canada Northern Forestry Ctr Canadian Forest Serv Edmonton AB Canada

In western Canada, decades of oil-and-gas exploration have fragmented boreal landscapes with a dense network of linear forest disturbances (seismic lines). These seismic lines are implicated in the decline in wildlife populations that are adapted to function in unfragmented forest landscapes. In particular, anthropogenic disturbances have led to a decline of woodland caribou populations due to increasing predator access to core caribou habitat. Restoration of seismic lines aims to reduce the landscape fragmentation and stop the decline of caribou populations. However, planning restoration in complex landscapes can be challenging because it must account for a multitude of diverse aspects. To assist with restoration planning, we present a spatial network optimization approach that selects restoration locations in a fragmented landscape while addressing key environmental and logistical constraints. We applied the model to develop restoration scenarios in the Redrock-Prairie Creek caribou range in northwestern Alberta, Canada, which includes a combination of caribou habitat and active oil-and-gas and timber extraction areas. Our study applies network optimization at two distinct scales to address both the broad-scale restoration policy planning and project-level constraints at the level of individual forest sites. We first delineated a contiguous set of coarse-scale regions where restoration is most cost-effective and used this solution to solve a fine-scale network optimization model that addresses environmental and logistical planning constraints at the level of forest patches. Our two-tiered approach helps address the challenges of fine-scale spatial optimization of restoration activities. An additional coarse-scale optimization step finds a feasible starting solution for the finescale restoration problem, which serves to reduce the time to find an optimal solution. The added coarse-scale spatial constraints also make the fine-scale restoration solution align with th

关键词： Network optimization Seismic lines Woodland caribou Access to habitat Spatial connectivity linear programming

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Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices

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ENERGY 2023年第PartE期263卷

作者： Gassi, Karim Bio Baysal, Mustafa Yildiz Tech Univ Smart Home Lab TR-34220 Istanbul Turkiye

The hereby study combines a reinforcement learning machine and a myopic optimization model to improve the real-time energy decisions in microgrids with renewable sources and energy storage devices. The reinforcement learning-based agent is built as an actor-critic agent making the aggregated near-optimal charging/discharging energy decisions of the microgrid energy storage devices from a discrete action space relying on a reward related to the microgrid online optimal objective function value. The next step time energy levels of storage devices are then computed and provided to the myopic optimization-based decision-making model as parameters which optimally find the incurred power flow within the microgrid minimizing the real-time microgrid energy cost. The real-time measurement of stochastic parameters of the microgrid coupled with the current energy levels of electrical and heat storage are input to the artificially intelligent machine as observations states. The actor-critic agent approximators are modeled as deep neural networks optimized using the Adam gradient descent algorithm with a gradient threshold. Although the proposed model with a 2-kWh increment of the charging/discharging energy training is time-consuming, it has been able at 100% to optimally make microgrid energy decisions and improve online energy decisions by 90.98% compared to the myopic model alone.

关键词： Actor-critic Reinforcement learning linear programming Microgrid Energy management system Power flow

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Distributed Inverse Optimal Control for Discrete-Time Nonlinear Multi-Agent Systems

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IEEE CONTROL SYSTEMS LETTERS 2021年第6期5卷 2096-2101页

作者： Belfo, Joao P. Aguiar, A. Pedro Lemos, Joao M. Univ Lisbon Inst Super Tecn INESC ID P-1000029 Lisbon Portugal Univ Porto Fac Engn SYSTEC P-4200465 Porto Portugal

This letter describes a robust distributed inverse optimal control framework for a multi-agent discrete-time nonlinear system, where the dynamics of each agent is directly affected by terms that depend on the state and input of the neighborhood agents and other disturbance signals. The individual local cost is formulated and a control solution for each agent is derived considering an inverse optimal control approach. To address the interaction between the agents, a coordination method based on a non-cooperative game is proposed. Using Lyapunov and Input-to-State Stability (ISS) arguments, we derive conditions under which the proposed game converges to a fixed point and the overall multi-agent system is ISS with respect to the disturbance signals. Simulation results for a coupled pendula system are presented.

关键词： Optimal control Optimization Multi-agent systems Heuristic algorithms Topology Games linear programming Cooperative control distributed control game theory robust control stability of nonlinear systems

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A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

IET ENERGY SYSTEMS INTEGRATION

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IET ENERGY SYSTEMS INTEGRATION 2021年第3期3卷 285-294页

作者： Bai, Linquan Xue, Yaosuo Xu, Guanglin Dong, Jin Olama, Mohammed M. Kuruganti, Teja Univ N Carolina Syst Engn & Engn Management Charlotte NC 28223 USA Oak Ridge Natl Lab Electrificat & Energy Infrastruct Div Oak Ridge TN USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN USA

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

关键词： distributed photovoltaics building thermal dynamics stochastic programming probability distributed power generation robust control mixed-integer convex optimisation problem optimisation data-driven distributionally robust optimisation approach distributed photovoltaic systems smart buildings complicated power system operation data-driven network optimisation approach integer programming distribution systems distribution networks linear programming convex programming uncertain parameters heat gain distribution system operation data-driven model building management systems outdoor temperature distributed PVs probability distributions photovoltaic power systems distributed energy resources coordinated control chance-constrained programming demand-side resources

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MILP-based customer-oriented E-Fleet charging scheduling platform

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IET SMART GRID 2021年第2期4卷 215-223页

作者： Akaber, Parisa Hughes, Timothy Sobolev, Sergey Siemens Plc Smart Infrastruct Newcastle Upon Tyne Tyne & Wear England

Conventional road transport is a significant contributor to greenhouse gas emissions and air pollution. Electric mobile assets (E-buses, E-trucks, E-taxis, and even E-ferries) are an efficient, low noise, low emission alternative which could-if operated alongside significant renewable energy capacity-help to improve air quality and avoid catastrophic global warming. In this study, a Mixed Integer linear programming-based solution for operating a fleet of E-mobile assets has been proposed, considering technical and operational characteristics of different fleet elements. It takes into account customer (E-fleet operator) objectives, such as load balancing and charging cost minimization. Furthermore, the E-depot charging capacity increase achieved through integrating an energy storage system has been studied and discussed. The results of the optimization have been discussed and reported using a case study (demonstrating peak reduction of up to 50% and total charging reduction of 27%) and the benefits of operating an E-fleet using the proposed solution have been discussed from economic and technical perspectives.

关键词： air pollution air pollution control battery powered vehicles integer programming linear programming renewable energy sources scheduling transportation

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Scheduling of pumped-storage hydro generator operation in balancing group with wind farms

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IEEJ Transactions on Power and Energy 2021年第2期141卷 79-87页

作者： Kaneko, Akihisa Inagaki, Maiko Fujimoto, Yu Hayashi, Yasuhiro Nonaka, Shunsuke Waseda University 63-6-B 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Electric Power Development Co. Ltd. 1-9-88 Chigasaki Chigasaki253-0041 Japan

To utilize renewable energy systems (RES) as main generators in power system, power system operators need to maintain reserve power to compensate the uncertain output of RES, such as wind farms (WF). This paper focuses on the balancing group (BG) composed of WF and a pumped-storage hydro generator (PSHG). In the BG operation, power system operators need to schedule the operation of PSHG to minimize the imbalance risk caused by the prediction error of WF output while considering the system specific constraints of PSHG, e.g. the mechanical constraint on pump-up/down switching. This paper proposes a method to schedule the PSHG operation using the interval prediction results of the WF output for alleviating the imbalance risk the proposed scheduling scheme is formulated based on the linear programming. To verify the effectiveness of the proposed method, the amount of imbalance and the net generation of the BG were evaluated based on numerical experiments under two types of situations with different characteristics in WF output. The experimental results reveal the effectiveness of the proposed method. © 2021 The Institute of Electrical Engineers of Japan.

关键词： linear programming

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Consistency Center-Based Deep Cross-Modal Hashing for Multisource Remote Sensing Image Retrieval

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Sun, Yuxi Ye, Yunming Kang, Jian Fernandez-Beltran, Ruben Li, Xutao Xiong, Zhenyu Huang, Xu Plaza, Antonio Harbin Inst Technol Dept Comp Sci Shenzhen 518055 Peoples R China Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Univ Murcia Dept Comp Sci & Syst Murcia 30100 Spain Naval Aviat Univ Res Inst Informat Fus Yantai 264001 Peoples R China Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10003 Spain

Cross-modal hashing aims to retrieve similar images from large-scale Earth observation (EO) data archives, which typically contain multiple satellite sources of remote sensing (RS) images. However, existing cross-modal hashing methods primarily focus on dual-source RS images and often face two main limitations when retrieving multisource RS images. First, these methods exhibit significant redundancy as they require handling all possible dual-source combinations in multisource RS images. Second, they often rely on pairwise or triplet image sources to construct objective functions, which are not significantly effective in reducing the discrepancies among multiple RS image sources. To address these limitations, we propose a novel consistency center-based deep cross-modal hashing method for multisource RS image retrieval called C(2)Hash. Our C(2)Hash employs a multibranch hashing network to directly encode multisource RS images into unified hash codes, thereby offering higher processing efficiency. Furthermore, C(2)Hash introduces consistency centers to construct a novel objective function. The consistency center represents the shared semantic features among similar multisource RS images and is generated by a label hashing network. The objective function encourages similar multisource RS images to approach the same consistency center to align all image sources in a unified Hamming space. Our method can effectively reduce the discrepancies across multiple image sources and generate unified hash codes. To evaluate its effectiveness, we construct a new multisource RS image dataset called MSRSI, comprising five different types of image sources. We conduct comprehensive experiments to demonstrate the superior performance of our method on the MSRSI dataset (https://***/sunyuxi/C2Hash).

关键词： Image retrieval Codes linear programming Semantics Satellites Task analysis Training Content-based remote sensing (RS) image retrieval deep supervised hashing learning to hash multisource remote sensing images

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Sharp Bounds on Functionals of the Joint Distribution in the Analysis of Treatment Effects

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2021年第2期39卷 532-546页

作者： Russell, Thomas M. Univ Toronto Dept Econ 150 St George St Toronto ON M5S 3G7 Canada

This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate additional model constraints. However, computational considerations for the method are found to be important and are discussed in detail. for this article are available online.

关键词： Convex optimization linear programming Optimal transport Partial identification Random set theory

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Data Selection in Neural Networks

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2021年 2卷 522-534页

作者： Ferreira, Jonathas O. Mendonca, Marcele O. K. Diniz, Paulo S. R. Univ Fed Rio de Janeiro Dept Elect & Comp Engn BR-21941972 Rio De Janeiro RJ Brazil

In the era of big data, profitable opportunities are becoming available for many applications. As the amount of data keeps increasing, machine learning becomes an attractive tool to analyze the information acquired. However, harnessing meaningful data remains a challenge. The machine learning tools employed in many applications apply all training data without taking into consideration how relevant are some of them. In this paper, we propose a data selection strategy for the training step of Neural Networks to obtain the most significant data information and improve algorithm performance during training. The approach proposes a data-selection strategy applied to classification and regression problems leading to computational savings and classification error reduction. Based on open datasets, including a deep neural network case, the examples corroborate the effectiveness of the proposed approach.

关键词： Training Artificial neural networks linear programming Signal processing algorithms Neurons Biological neural networks Signal processing Neural network data selection gradient descent deep learning

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