Proceedings of the Eleventh ACM International Conference on Future Energy Systems最新文献

{"title":"Negative Cycle Canceling with Neighborhood Heuristics for the Wind Farm Cabling Problem","authors":"Sascha Gritzbach, D. Wagner, Matthias Wolf","doi":"10.1145/3396851.3397754","DOIUrl":"https://doi.org/10.1145/3396851.3397754","url":null,"abstract":"The Wind Farm Cabling Problem (WCP) aims at finding the cost-minimal inter-array cable routing, also known as internal cable layout, of a wind farm so that all turbine generation is transmitted to the substations. For each possible connection in the wind farm, one of several cable types can be selected. Each cable type comes with a thermal capacity and unit length costs. WCP can be modeled as a graph theoretic minimum-cost flow problem with a step-cost function on each edge. We extend a deterministic \"hill-climbing\" heuristic from the literature. This heuristic runs into local minima from which it is not able to recover. We embed this algorithm into a framework which involves strategies for escaping these minima. These escaping strategies allow the heuristic to descend into other, possibly better, minima. We design three such strategies and provide an extensive statistical evaluation comparing these strategies. The best combination of strategies is evaluated against Gurobi 9.0.0 on a Mixed-integer Linear Program formulation and a Simulated Annealing-based heuristic from the literature on publicly available synthetic benchmark sets. Our simulations show that our framework works exceptionally well on the largest benchmark instances where it provides better solution within 15 minutes than Gurobi within one day on 80 % of the input instances. The simulations on the benchmark sets are complemented by a case study on the world's soon-to-be largest offshore wind farm: Hornsea One.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128583157","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}

{"title":"ORC","authors":"Bo Sun, Tongxin Li, S. Low, D. Tsang","doi":"10.1145/3396851.3397727","DOIUrl":"https://doi.org/10.1145/3396851.3397727","url":null,"abstract":"There is an increasing need for spatial and temporal schedule tailored to the requests and preferences of electric vehicles (EVs) in a network of charging stations. From the perspective of a charging network operator, this paper considers an online decision-making problem that recommends charging stations and the corresponding energy prices to sequential EV arrivals, and schedules the charging allocation to maximize the expected total revenue. To address the uncertainties from future EV arrivals and EVs' choices with respective to recommendations, we propose an Online Recommendation and Charging schedule algorithm (ORC) that is parameterized by a value function for customized designs. Under the competitive analysis framework, we provide a sufficient condition on the value function that can guarantee ORC to be online competitive. Moreover, we design a customized value function based on the sufficient conditions in an asymptotic case, and then rigorously prove the competitive ratio of ORC in the general case. Through extensive experiments, we show that ORC achieves significant increase of revenues compared to benchmark online algorithms.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"70 5 PT.1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132054372","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}

{"title":"PriorityBucket","authors":"Xiang Shi, Fa Zhang, Zhiyong Liu","doi":"10.1145/3396851.3402923","DOIUrl":"https://doi.org/10.1145/3396851.3402923","url":null,"abstract":"Page load time is a web performance metric that directly impacts user experience. It measures the time it takes to fully load a web page after making a request. However, as the first visual feedback of a web page, first rendering time is also a key metric to satisfy the users. In this paper, we focus on speeding up the first rendering time in page loading from the perspective of protocol improvements. We observe that by prioritizing the streams that make up the critical rendering path in Multipath-QUIC (MPQUIC), the first rendering time can be effectively reduced. Therefore, we propose PriorityBucket - an MPQUIC scheduler that provides stream prioritization in the transport layer. By comparing to the existing schedulers of MPQUIC, we show its effectiveness in reducing the first rendering time and page load time of web pages in different path heterogeneity using the Chrome browser. Results show that PriorityBucket can reduce the first rendering time and page load time up to 34% and 35% respectively in high path heterogeneity when loading Bing.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123878652","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}

{"title":"Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination","authors":"Tongxin Li, S. Low, A. Wierman","doi":"10.1145/3396851.3397725","DOIUrl":"https://doi.org/10.1145/3396851.3397725","url":null,"abstract":"Consider a system operator that wishes to optimize its objectives over time subject to operational constraints as well as private constraints of controllable loads managed by an aggregator. In this paper, we design a real-time feedback signal for the aggregator to quantify and communicate its available flexibility to the system operator. The proposed feedback signal at each time is the conditional probability of future feasible trajectories that will be enabled by the operator's decision. We show that it is the unique distribution that maximizes a system capacity for flexibility. It allows the system operator to maintain feasibility and enhance future flexibility while optimizing its objectives. We illustrate how the design can be used by the system operator to perform online cost minimization and real-time capacity estimation, while provably satisfying the private constraints of the loads.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128921633","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}

{"title":"MELODY","authors":"Zimu Zheng, Daqi Xie, Jie Pu, Feng Wang","doi":"10.1145/3396851.3397689","DOIUrl":"https://doi.org/10.1145/3396851.3397689","url":null,"abstract":"It is well-known that the HVAC (heating, ventilation and air conditioning) dominates electricity consumption in commercial buildings. Existing study on HVAC has shown that it is important to accurately quantify the performance profile of a chiller, namely coefficient of performance (COP), and data-driven COP prediction has been recently proposed. However, the task definition for COP prediction, e.g., the number of needed models and the context when the model should be used, is left as an open question. We propose a framework of Metadata-driven Multi-task COP Prediction with Adaptive Task Definition Methodology (MELODY) which defines and learns multiple COP tasks. To the best of our knowledge, this is the first method that adaptively defines COP prediction tasks according to various datasets. As such, this method can select specific COP models under varied contexts and estimate COP. A key idea is to use metadata to dynamically define multiple tasks. We provide a formal definition of metadata and two sources and methods to extract metadata. We evaluate the performance of our scheme by applying it to real-world data, spanning four months obtained from multiple chillers across eight buildings in two large industrial parks in an international metropolis. The results show that our solution outperforms state-of-the-art COP prediction methods and is able to save on 252 MWh of electricity consumption for one month in each of the eight buildings, which is an improvement of over 35% compared to the current mode of operation of the chillers in the buildings.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114554310","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}

{"title":"Design and Development of Brownout Control Strategy Using End-Point Load Control","authors":"Adithya Ramanujam, Mahesh Parihar, Suchitra Swain, K. Ramamritham","doi":"10.1145/3396851.3397738","DOIUrl":"https://doi.org/10.1145/3396851.3397738","url":null,"abstract":"Imbalances in electricity supply and demand are a significant problem in developing countries resulting in rolling blackouts/load shedding. Due to the inconvenience caused by blackouts, several brownout (partial blackout) strategies have been proposed. In this paper, we resort to keeping the electricity demand of a household within a specified threshold. Our approach combines two distinct methods of end-point load (appliance) control: priority-based techniques and combinatorial optimization-based techniques to create a hybrid approach that gives users flexibility in configuring their preferences on-the-fly. We have quantified the user preferences using the Analytical Hierarchy Process, which is useful for solving such Multi-Criteria Decision-Making problems.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014628","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}

{"title":"Using Satellite and Aerial Imagery for Identification of Solar PV: State of the Art and Research Opportunities","authors":"J. Hoog, S. Maetschke, Peter Ilfrich, Ramachandra Rao Kolluri","doi":"10.1145/3396851.3397681","DOIUrl":"https://doi.org/10.1145/3396851.3397681","url":null,"abstract":"Solar photovoltaic (PV) is the fastest growing form of energy generation today, and many countries are seeing significant uptake of distributed solar PV on the rooftops of homes and businesses. However, many of these systems are not accurately registered, and central records of distributed solar PV are often not up-to-date. At the same time, high levels of solar PV are introducing challenges for many stakeholders in the energy sector, such as market operators and network operators, who need to forecast total rooftop solar PV generation across entire regions. One possible solution to this problem is to identify existing solar PV generation systems using overhead satellite and aerial imagery. While there have been early promising attempts in this direction, there are nevertheless many important research challenges that remain to be addressed. In this paper we survey the state of the art in this nascent area, describe the challenges that exist, and advocate for novel research questions that are worthy of further exploration. By identifying these areas of interest we aim to generate greater awareness of the potential value of satellite and aerial imagery for identification of solar PV, which will ultimately facilitate large scale uptake of solar PV and other renewable generation technologies.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122109185","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}

{"title":"Discrete and stochastic coalitional storage games","authors":"Diego Kiedanski, A. Orda, D. Kofman","doi":"10.1145/3396851.3397729","DOIUrl":"https://doi.org/10.1145/3396851.3397729","url":null,"abstract":"As of today, energy storage for residential consumers represents a considerable investment that is not guaranteed to be profitable. Shared investment models in which a group of consumers jointly acquires energy storage have been proposed in the literature to increase the attractiveness of these devices. Such models naturally employ concepts of cooperative game theory. In this paper, we extend the state-of-the-art cooperative game for modeling the shared investment in storage by adding two crucial extensions: stochasticity of the load and discreetness of the storage device capacity. As our goal is to increase storage capacity in the grid, the number of devices that would be acquired by a group of players that cooperate according to our proposed scheme is compared to the number of devices that would be bought by consumers acting individually. Under the same criteria of customer profitability, simulations using real data reveal that our proposed scheme can increase the deployed storage capacity between 100% and 250%.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134255048","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}

{"title":"Adaptive Control of Plug-in Electric Vehicle Charging with Reinforcement Learning","authors":"Abdullah Al Zishan, Moosa Moghimi Haji, Omid Ardakanian","doi":"10.1145/3396851.3397706","DOIUrl":"https://doi.org/10.1145/3396851.3397706","url":null,"abstract":"This paper proposes an adaptive additive-increase multiplicative-decrease (AIMD)-like algorithm for controlled charging of plug-in electric vehicles in a power system. The proposed algorithm is decentralized and model-free, and relies on congestion signals received from sensors deployed across the network to avoid congestion. We use multi-agent reinforcement learning to dynamically adjust the parameters of the adaptive AIMD algorithm assuming that charging points are independent agents. We adopt imitation learning to pre-train these agents and an off-policy actor-critic deep reinforcement learning algorithm to determine the optimal control in the online setting. Simulation results obtained in a parking station with several charging points corroborate that the proposed algorithm closely tracks the available capacity of the network while avoiding line or transformer overloading, and outperforms the AIMD algorithm and other baselines in terms of utilization.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132651743","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}

{"title":"A Photovoltaic System Investment Game for Assessing Network Hosting Capacity Allocations","authors":"Yiju Ma, Daniel Gebbran, Archie C. Chapman, G. Verbič","doi":"10.1145/3396851.3397736","DOIUrl":"https://doi.org/10.1145/3396851.3397736","url":null,"abstract":"The rapid rise of PV installations in low-voltage (LV) distribution networks means that they are likely to exceed network hosting capacity. For this reason, distribution network service providers (DNSP) have begun to mandate connection codes, such as inverter Volt/Var control and/or PV active power curtailment, to mitigate the resulting network problems. This approach manages the network state, but may cause an existing PV system to become inefficient as it is curtailed more often. This paper investigates the effects on overall economic efficiency and individual customer welfare of natural uncoordinated rooftop PV investment processes that arise when customers invest in PV systems independently to maximize their individual welfare. We develop a novel game-theoretic framework that computes the annual payoffs to customers for different PV investment sizes, given the installations of other customers. This calculation is based on an optimal AC power flow model that includes inverter connection standards that link customers' annual payoffs via their effects on AC network voltages and consequent PV curtailment responses. We show that the interaction of PV investments produces a concave potential game with continuous action sets, which has a pure Nash equilibrium that can be found using an adaptive learning process. Then, to evaluate the efficiency of the investments under the game model, we compute an centrally-coordinated PV investment profile, found by solving an optimal PV sizing problem that maximizes social welfare across all customers. Comparing the value of investment patterns for the game and the centrally-coordinated optimization shows: (i) the inefficiency of the Nash equilibrium is 1.4, which indicates the efficiency loss resulting from uncoordinated PV investments, and (ii) the inequity of a skewed distribution of benefits, penalising customers closer to the distribution transformer and benefiting those towards the end of the feeder. This model provides a quantitative tool for evaluating policies and regulations that improved coordination and allocation of PV hosting capacity (and that of other energy distributed energy resources) between customers on LV feeders.","PeriodicalId":442966,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Future Energy Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131246407","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}