Proceedings of the 14th ACM International Conference on Future Energy Systems最新文献_第5页

Data-Driven Inverse Optimization for Marginal Offer Price Recovery in Electricity Markets 电力市场边际出价恢复的数据驱动逆优化

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-02-10 DOI: 10.1145/3575813.3597356

Zhirui Liang, Y. Dvorkin

引用次数: 0

Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks 电动汽车充电网络联合定价与调度的近最优在线算法

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-01-11 DOI: 10.1145/3575813.3576878

Roozbeh Bostandoost, Bo Sun, Carlee Joe-Wong, M. Hajiesmaili

{"title":"Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks","authors":"Roozbeh Bostandoost, Bo Sun, Carlee Joe-Wong, M. Hajiesmaili","doi":"10.1145/3575813.3576878","DOIUrl":"https://doi.org/10.1145/3575813.3576878","url":null,"abstract":"With the rapid acceleration of transportation electrification, public charging stations are becoming vital infrastructure in smart sustainable cities to provide on-demand electric vehicle (EV) charging services. As more consumers seek to utilize public charging services, the pricing and scheduling of such services will become vital, complementary tools to mediate competition for charging resources. However, determining the right prices to charge is difficult due to the online nature of EV arrivals. This paper studies a joint pricing and scheduling problem for the operator of EV charging networks with limited charging capacity and time-varying energy costs. Upon receiving a charging request, the operator offers a price, and the EV decides whether to accept the offer based on its own value and the posted price. The operator then schedules the real-time charging process to satisfy the charging request if the EV admits the offer. We propose an online pricing algorithm that can determine the posted price and EV charging schedule to maximize social welfare, i.e., the total value of EVs minus the energy cost of charging stations. Theoretically, we prove the devised algorithm can achieve an order-optimal competitive ratio under the competitive analysis framework. Practically, we show the empirical performance of our algorithm outperforms other benchmark algorithms in experiments using real EV charging data.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134314427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Equitable Network-Aware Decarbonization of Residential Heating at City Scale 城市尺度住宅供暖的公平网络感知脱碳

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-01-11 DOI: 10.1145/3575813.3576870

Adam Lechowicz, Noman Bashir, John Wamburu, M. Hajiesmaili, P. Shenoy

{"title":"Equitable Network-Aware Decarbonization of Residential Heating at City Scale","authors":"Adam Lechowicz, Noman Bashir, John Wamburu, M. Hajiesmaili, P. Shenoy","doi":"10.1145/3575813.3576870","DOIUrl":"https://doi.org/10.1145/3575813.3576870","url":null,"abstract":"Residential heating, primarily powered by natural gas, accounts for a significant portion of residential sector energy use and carbon emissions in many parts of the world. Hence, there is a push towards decarbonizing residential heating by transitioning to energy-efficient heat pumps powered by an increasingly greener and less carbon-intensive electric grid. However, such a transition will add additional load to the electric grid triggering infrastructure upgrades, and subsequently erode the customer base using the gas distribution network. Utilities want to guide these transition efforts to ensure a phased decommissioning of the gas network and deferred electric grid infrastructure upgrades while achieving carbon reduction goals. To facilitate such a transition, we present a network-aware optimization framework for decarbonizing residential heating at city scale with an objective to maximize carbon reduction under budgetary constraints. Our approach operates on a graph representation of the gas network topology to compute the cost of transitioning and select neighborhoods for transition. We further extend our approach to explicitly incorporate equity and ensure an equitable distribution of benefits across different socioeconomic groups. We apply our framework to a city in the New England region of the U.S., using real-world gas usage, electric usage, and grid infrastructure data. We show that our network-aware strategy achieves 55% higher carbon reductions than prior network-oblivious work under the same budget. Our equity-aware strategy achieves an equitable outcome while preserving the carbon reduction benefits of the network-aware strategy.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127288701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Adapting Datacenter Capacity for Greener Datacenters and Grid 为更环保的数据中心和电网调整数据中心容量

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-01-09 DOI: 10.1145/3575813.3595197

Liuzixuan Lin, A. Chien

{"title":"Adapting Datacenter Capacity for Greener Datacenters and Grid","authors":"Liuzixuan Lin, A. Chien","doi":"10.1145/3575813.3595197","DOIUrl":"https://doi.org/10.1145/3575813.3595197","url":null,"abstract":"Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%–5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%–7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%–12.6%, 1.56x–1.26x better than the best local, online approach’s results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126711647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning 利用强化学习实现公平太阳能光伏弃风的无模型方法

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2022-12-13 DOI: 10.1145/3575813.3576871

Zhuo Wei, F. D. Nijs, Jinhao Li, Hao Wang

引用次数: 3

AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models 自动光伏预测:使用预先训练模型集合的有限信息自动光伏预测

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2022-12-13 DOI: 10.1145/3575813.3597348

Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, R. Mikut, V. Hagenmeyer

{"title":"AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models","authors":"Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, R. Mikut, V. Hagenmeyer","doi":"10.1145/3575813.3597348","DOIUrl":"https://doi.org/10.1145/3575813.3597348","url":null,"abstract":"Forecasting the power generation of locally distributed PhotoVoltaic plants is vital for the efficient operation of Smart Grids. The automated design of such models for PV plants includes two challenges: First, information about the PV mounting configuration (i.e. tilt and azimuth angles) is often missing. Second, for new PV plants, the amount of historical data available to train a PV model is limited (cold-start problem). Therefore, we aim to address these two problems while reaching an accuracy comparable to methods that require such information and historical data, and propose AutoPV. AutoPV is a weighted ensemble of models that represent different PV mounting configurations. This representation is achieved by pre-training each model on data of a separate PV plant scaled by its peak power rating. To tackle the cold-start problem, we initially weight each model in the ensemble equally. To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the error. AutoPV is advantageous since the unknown PV mounting configuration is implicitly reflected in the ensemble weights, and only the PV plant’s peak power rating is required to re-scale the ensemble’s output. The ensemble approach also allows the representation of mixed-oriented PV plants, as the multiple mounting configurations can be reflected proportionally in the weighting. AutoPV’s automated weight adaptation and cold-start capability are essential for real-world applications to keep pace with the expansion of the PV power generation capacity in future energy systems. It is shown that for a real-world data set, the accuracy of AutoPV is comparable to a non-cold-start model and outperforms (quasi-)cold-start capable models.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"54 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113959627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning BEAR:控制和强化学习的物理原则构建环境

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2022-11-27 DOI: 10.1145/3575813.3576877

Chi Zhang, Yu Shi, Yize Chen

{"title":"BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning","authors":"Chi Zhang, Yu Shi, Yize Chen","doi":"10.1145/3575813.3576877","DOIUrl":"https://doi.org/10.1145/3575813.3576877","url":null,"abstract":"Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dynamical model and energy management task simulation and evaluation platform has arguably slowed the progress in developing advanced and dedicated reinforcement learning (RL) and control algorithms for building operation tasks. Here we propose “BEAR”, a physics-principled Building Environment for Control and Reinforcement Learning. The platform allows researchers to benchmark both model-based and model-free controllers using a broad collection of standard building models in Python without co-simulation using external building simulators. In this paper, we discuss the design of this platform and compare it with other existing building simulation frameworks. We demonstrate the compatibility and performance of BEAR with different controllers, including both model predictive control (MPC) and several state-of-the-art RL methods with two case studies. BEAR is available at https://github.com/chz056/BEAR.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115452040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Scalable Bilevel Framework for Renewable Energy Scheduling 可再生能源调度的可扩展双层框架

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2022-11-25 DOI: 10.1145/3575813.3595199

Dongwei Zhao, V. Dvorkin, S. Delikaraoglou, A. Lamadrid L, A. Botterud

{"title":"A Scalable Bilevel Framework for Renewable Energy Scheduling","authors":"Dongwei Zhao, V. Dvorkin, S. Delikaraoglou, A. Lamadrid L, A. Botterud","doi":"10.1145/3575813.3595199","DOIUrl":"https://doi.org/10.1145/3575813.3595199","url":null,"abstract":"Accommodating the uncertain and variable renewable energy sources (VRES) in electricity markets requires sophisticated and scalable tools to achieve market efficiency. To account for the uncertain imbalance costs in the real-time market while remaining compatible with the existing sequential market-clearing structure, our work adopts an uncertainty-informed adjustment toward the VRES contract quantity scheduled in the day-ahead market. This mechanism requires solving a bilevel problem, which is computationally challenging for practical large-scale systems. To improve the scalability, we propose a technique based on strong duality and McCormick envelopes, which relaxes the original problem to linear programming. We conduct numerical studies on both IEEE 118-bus and 1814-bus NYISO systems. Results show that the proposed relaxation can achieve good performance in accuracy (0.7%-gap in the system cost wrt. the least-cost stochastic clearing benchmark) and scalability (solving the NYISO system in minutes). Furthermore, the benefit of this bilevel VRES-quantity adjustment is more significant under higher penetration levels of VRES (e.g., 70%), under which the system cost can be reduced substantially compared to a myopic day-ahead offer strategy of VRES.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132221959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings of the 14th ACM International Conference on Future Energy Systems 第14届美国计算机学会未来能源系统国际会议论文集

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 1900-01-01 DOI: 10.1145/3575813

引用次数: 0