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One of the main objectives of a peer-to-peer energy market is to efficiently manage distributed energy resources while creating additional financial benefits for the participants. Cooperative game theory offers such a framework, and the Shapley value, a cooperative game payoff allocation based on the participants’ marginal contributions made to the local energy coalition, is shown to be fair and efficient. However, its high computational complexity limits the size of the game. In order to improve this peer-to-peer cooperative scheme’s scalability, this paper investigates and adapts a stratified sampling method for the Shapley value estimation. It then proposes a multi-step sampling strategy to further reduce the computation time by dividing the samples into incremental parts and terminating the sampling process once a certain level of estimation performance is achieved. Finally, selected case studies demonstrate the effectiveness of the proposed method, which is able to scale up the game from 20 players to 100 players.

Energy forecasting has attracted enormous attention over the last few decades, with novel proposals related to the use of heterogeneous data sources, probabilistic forecasting, online learning, etc. A key aspect that emerged is that learning and forecasting may highly benefit from distributed data, though not only in the geographical sense. That is, various agents collect and own data that may be useful to others. In contrast to recent proposals that look into distributed and privacy-preserving learning (incentive-free), we explore here a framework called regression markets. There, agents aiming to improve their forecasts post a regression task, for which other agents may contribute by sharing their data for their features and get monetarily rewarded for it. The market design is for regression models that are linear in their parameters, and possibly separable, with estimation performed based on either batch or online learning. Both in-sample and out-of-sample aspects are considered, with markets for fitting models in-sample, and then for improving genuine forecasts out-of-sample. Such regression markets rely on recent concepts within interpretability of machine learning approaches and cooperative game theory, with Shapley additive explanations. Besides introducing the market design and proving its desirable properties, application results are shown based on simulation studies (to highlight the salient features of the proposal) and with real-world case studies.

This paper proposes a regression market for wind agents to monetize data traded among themselves for wind power forecasting. Existing literature on data markets often treats data disclosure as a binary choice or modulates the data quality based on the mismatch between the offer and bid prices. As a result, the market disadvantages either the data sellers due to the overestimation of their willingness to disclose data, or the data buyers due to the lack of useful data being provided. Our proposed regression market determines the data payment based on the least absolute shrinkage and selection operator (lasso), which not only provides the data buyer with a means for selecting useful features, but also enables each data seller to individualize the threshold for data payment. Using both synthetic data and real-world wind data, the case studies demonstrate a reduction in the overall losses for wind agents who buy data, as well as additional financial benefits to those who sell data., Comment: Accepted to PSCC 2022. Will be included in a special issue of the journal Electric Power Systems Research (EPSR)

International audience; In this presentation we will show the research directions and innovative solutions developed in the European Horizon 2020 project Smart4RES (http://www.smart4res.eu) for better modelling and forecasting of weather variables necessary to optimise the integration of renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES started in November 2019 and gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to reach very high RES penetrations in power grids of 2030 and beyond, through thematic objectives including:• Improvement of weather forecasting with focus on variables that are important for RES applications,• Improvement of RES power forecasting and better estimation of uncertainties, • Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models,• New data-driven optimization and decision-aid tools for market and grid management applications.• Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). Smart4RES focuses both on improving forecasting models of weather (e.g. physical models, data assimilation, Large Eddy Simulation, enabling weather forecasts seamless) and RES production (e.g. seamless models, highly resolved predictions), and on addressing applications in power grids. Developments in the project have been formalized in Use Cases that cover a large range of time frames, technologies and geographical scales. For example, use-cases on power grids refer to the provision of ancillary services to the upper-level grid (e.g., balancing power) and the local grid (e.g., voltage control and congestion management), where the accurate forecasts of variable generation are key for accurate decision-making. A grid state forecasting will quantify dynamically the flexibility potential of RES in distribution grids. Collaborative forecasting investigates the improvement associated to local data sharing between distributed RES plants. This data sharing paves the way to a data market where agents exchange measurements, predictions or other types of valuable data. Lastly, data-driven approaches will streamline decision-making by simplifying the model chain of bidding RES production, storage dispatch or predictive management electricity grids. They will also provide interpretable hindsight to decision-makers by integrating the decisions of experts (human-in-the-loop) and will be tested in realistic laboratory conditions (software-in-the-loop).In this presentation we focus on the work done for improving modelling and forecasting of weather variables with accent to wind energy; i.e. through innovative measuring set-ups; through the development of seamless numerical weather prediction (NWP) approaches to be able to couple outputs of NWP models with different resolutions; through ultra-high resolution NWPs based on Large Eddy Simulation. We present results using data from real world test cases considered in the project. Finally, we assess how the new forecasting products may bring value to the applications.

The fast increase in distributed energy resources (DERs), including renewable sources, flexible loads, and energy storage, has led to new technological and economic challenges and opportunities for power networks. The increasing flexibility and controllability of DERs, coupled with advanced communication technologies, inspired the concept of peer-to-peer (P2P) energy trading or sharing among prosumers, proactive consumers with DERs that actively control their energy behaviors. The successful realization of P2P energy trading has the potential of both financially benefiting prosumers and reducing the grid’s operational burden. However, many technical, regulatory, and social challenges are standing in the way. This chapter explores a range of topics in regard to the P2P energy market, from its background and its structure, to the opportunities and challenges it faces.

Distributed energy storage (ES) is widely regarded as a tool to mitigate the supply-demand imbalance caused by intermittent generations and variable loads in a distribution network. Because a retail supplier generally offers a lower electricity sell price (e.g. feed-in tariff) than its buy price, ES owners are monetarily incentivized to store excess generation for later usage. However, since each ES owner typically optimizes the ES operation based on their individual usage profile, the joint load balancing effect of multiple ES systems becomes insignificant. This paper proposes an energy coalition, in which ES system owners operate collaboratively to minimize the total coalitional energy cost. Using cooperative game theory, the nucleolus and Shapley value are developed as methods to reward players. Finally, selected case studies compare the load balancing effectiveness of noncooperative and cooperative ES operations, and empirically demonstrate the nucleolus's stablizing characteristic, which incentivizes prosumers to stay within the grand coalition.

The advances in distributed renewable generation technologies in recent years have started to cause load balancing issues in power networks. Distributed energy storage (ES) systems, although seen as a tool to mitigate the stress on local networks, tend to be operated only to minimize the energy cost of their direct owner. In this paper, cooperative game theory is used to construct an energy grand coalition, in which ES system operations are optimized to minimize the coalitional energy cost. Case studies then show that forming energy coalitions is effective in reducing the variability of a local network load profile. The resulting cooperative game is mathematically proven to be balanced, which means the energy cost savings from the cooperative ES operation can be allocated to the players within the energy grand coalition in a manner that disincentivizes them from exiting from the grand coalition to form smaller coalitions.

Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the "clustered players" to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers., 6 pages, 4 figures, 2 tables. Accepted to the 13th IEEE PES PowerTech Conference, 23-27 June 2019, Milano, Italy

The type of oil used and the introduction of bioactive compounds could strongly influence the microstructure and physicochemical properties of Pickering emulsions, which have yet to be fully understood. Herein, oil-in-water (O/W) Pickering emulsions were stabilized by autofluorescent gelatin nanoparticles with either corn oil or medium chain triglyceride (MCT) oil as references for commonly studied edible oils. The microstructure of the emulsions with changes in nanoparticle concentration, oil fraction (Φ), and oil type was investigated with confocal laser scanning microscopy (CLSM). An increase in nanoparticle concentration resulted in an increased thickness of the adsorption layers and number of droplets. Increasing Φ helped to form compact gel-like emulsions and improve the oxidation stability of the emulsions. The high oil-water interfacial tension and surface-active long-chain fatty acids of corn oil caused sparse packing droplet interfaces. Hence, corn oil emulsions were generally less stable than MCT oil emulsions, which was further demonstrated through studies of water holding capacity (WHC), long-term storage stability, and rheology. Furthermore, the incorporation of β-carotene had negative effects on the formation and stability of the emulsions, especially for corn oil emulsions. The findings in this work may have important implications for exploiting Pickering emulsion formulations in real food products.

This paper presents an extensible and adaptable tool with which to study the use of cooperative game theory for allocating rewards to prosumers in peer-to-peer (P2P) energy trading systems. With the aim of expanding the use of renewable generation, the tool is a combination of the cooperative game theory platform and a specific game which focuses on a prosumer P2P network in a distribution network feeder. The final platform is able to run user defined P2P trading games, including the aforementioned one, over multiple trading intervals. It has two inbuilt allocation schemes which can be tested on the games for fairness and stability.

This study presents a roadmap for converting California's all-purpose (electricity, transportation, heating/ cooling, and industry) energy infrastructure to one derived entirely from wind, water, and sunlight (WWS) generating electricity and electrolytic hydrogen. California's available WWS resources are first evaluated. A mix of WWS generators is then proposed to match projected 2050 electric power demand after all sectors have been electrified. The plan contemplates all new energy from WWS by 2020, 80e85% of existing energy converted by 2030, and 100% by 2050. Electrification plus modest efficiency measures may reduce California's end-use power demand ~44% and stabilize energy prices since WWS fuel costs are zero. Several methods discussed should help generation to match demand. A complete conversion in California by 2050 is estimated to create ~220,000 more 40-year jobs than lost, eliminate ~12,500 (3800 e23,200) state air-pollution premature mortalities/yr, avoid $103 (31e232) billion/yr in health costs, representing 4.9 (1.5e11.2)% of California's 2012 gross domestic product, and reduce California's 2050 global climate cost contribution by $48 billion/yr. The California air-pollution health plus global climate cost benefits from eliminating California emissions could equal the $1.1 trillion installation cost of 603 GW of new power needed for a 100% all-purpose WWS system within ~7 (4e14) years.

Red fluorescent proteins (RFPs) are useful tools for live cell and multi-color imaging in biological studies. However, when labeling proteins in secretory pathway, many RFPs are prone to form artificial puncta, which may severely impede their further uses. Here we report a fast and easy method to evaluate RFPs fusion properties by attaching RFPs to an environment sensitive membrane protein Orai1. In addition, we revealed that intracellular artificial puncta are actually colocalized with lysosome, thus besides monomeric properties, pKa value of RFPs is also a key factor for forming intracellular artificial puncta. In summary, our current study provides a useful guide for choosing appropriate RFP for labeling secretory membrane proteins. Among RFPs tested, mOrange2 is highly recommended based on excellent monomeric property, appropriate pKa and high brightness.

In this study, a novel paper-based SERRS immunoassay based on magnetic separation and alkaline phosphatase (ALP) enzyme catalyzed hydrolysis reaction was developed. By using modified antibodies conjugated to magnetic beads, capture of mouse IgG followed by addition of ALP-labeled antibodies would form a sandwich-like immunoconjugate. After magnetic separation, 5-bromo-4-chloro-3-indolyl phosphate (BCIP), a low SERRS active compound, was added as the substrate for ALP to generate a high SERRS response. Detection was conducted on a silver colloid/PVP/filter paper SERS substrate by spotting a pre-aggregated silver colloid sol onto polyvinyl pyrrolidone (PVP) modified filter paper using a semi-automatic TLC sample applicator. The optimization of the highly SERS active paper-based substrate, dynamic hydrolysis process of BCIP, quantitative detection of IgG, and selectivity of the assay was studied in detail. By taking advantage of magnetic separation in order to decrease the background interference, the selective enzyme reaction involved in producing a highly SERRS active product could detect mouse IgG from 1 to 500 ng mL(-1) with a LOD of 0.33 ng mL(-1).