A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

We present a distributed computing architecture for smart grid management, composed of two applications at two different levels of the grid. At the high voltage level, we optimize operations using a stochastic unit commitment (SUC) model with hybrid time resolution. The SUC problem is solved with an asynchronous distributed subgradient method, for which we propose stepsize scaling and fast initialization techniques. The asynchronous algorithm is implemented in a high-performance computing cluster and benchmarked against a deterministic unit commitment model with exogenous reserve targets in an industrial scale test case of the Central Western European system (679 buses, 1037 lines, and 656 generators). At the distribution network level, we manage demand response from small clients through distributed stochastic control, which enables harnessing residential demand response while respecting the desire of consumers for control, privacy, and simplicity. The distributed stochastic control scheme is successfully tested on a test case with 10,000 controllable devices. Both applications demonstrate the potential for efficiently managing flexible resources in smart grids and for systematically coping with the uncertainty and variability introduced by renewable energy

Wind Farm Portfolio Optimization under Network Capacity Constraints

International audienceIn this article, we provide a new methodology for optimizing a portfolio of wind farms within a market environment, for two Market Designs (exogenous prices and endogenous prices). Our model is built on an agent based representation of suppliers and generators interacting in a certain number of geographic demand markets, organized as two tiered systems. Assuming rational expectation of the agents with respect to the outcome of the real-time market, suppliers take forward positions, which act as signals in the day-ahead market, to compensate for the uncertainty associated with supply and demand. Then, generators optimize their bilateral trades with the generators in the other markets. The Nash Equilibria resulting from this Signaling Game are characterized using Game Theory. The Markowitz Frontier, containing the set of efficient wind farm portfolios, is derived theoretically as a function of the number of wind farms and of their concentration. Finally, using a case study of France, Germany and Belgium, we simulate the Markowitz Frontier contour in the expected cost-risk plane

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Scarcity pricing and the missing European market for real-time reserve capacity

Scarcity pricing is a valuable step towards the evolution of electricity markets that rely increasingly on reserves for enabling the large-scale penetration of renewable resources. A real-time market for reserve capacity is essential in the implementation of scarcity pricing, in order to enable the back-propagation of the value of reserve capacity to forward markets for energy and reserve. Such a market for real-time reserve capacity does not exist currently in Europe. Consequently, the existing design of the European balancing market creates challenges for the valuation of reserves. We argue that the implementation of a real-time market for reserve capacity can be aligned with European legislation, and we describe how scarcity pricing based on operating reserve demand curves can be integrated in such a design. We discuss the ongoing scarcity pricing debate in Belgium, and highlight various implementation challenges. © 2020 The Author(s

Coupling Renewable Energy Supply with Deferrable Demand

The large-scale integration of renewable energy sources is advancing rapidly in numerous power systems. However, utilizing renewable resources at a bulk scale is hindered by the fact that these resources are neither controllable nor accurately predictable. Our analysis focuses on the cost of balancing power system operations in the presence of renewable resources and on the amount of operating reserves that is necessary for ensuring the reliable operation of the system. We also explore the extent to which demand-side flexibility can mitigate these impacts. We present a contract that couples the operations of renewable energy resources with deferrable loads that can shift a fixed amount of energy demand over a given time window. Various flexible energy consumption tasks can be characterized in this way, including electric vehicle charging or agricultural pumping.We use a two-stage stochastic unit commitment model for our analysis. The use of this model is justified by the fact that it is capable of quantifying the operating costs of the system and the amount of required capacity in order to face the increased uncertainty of daily operations. We present a dual decomposition algorithm for solving the model and various scenario selection algorithms for representing uncertainty. We present results for a reduced network of the California power system that consists of 124 generators, 225 buses and 375 lines. We fist validate the stochastic unit commitment policy that we derive from the stochastic optimization model by demonstrating that it outperforms deterministic unit commitment rules commonly used in practice. We demonstrate this superior performance for both a transmission-constrained as well as an unconstrained system for various types of uncertainty including network element failures as well as two levels of wind integration that roughly correspond to the 2012 and 2020 renewable energy integration targets of California. We then use the stochastic unit commitment model to quantify the impacts of coupling renewable energy supply with deferrable demand on operating costs and reserve requirements. We also demonstrate the superiority of coupling contracts to demand-side bidding in the day-ahead market which is due to the fact that demand bids fail to account for the inter-temporal dependency of deferrable demand

Analysis of distribution locational marginal prices

Low-voltage distribution networks are emerging as an increasingly important component of power system operations due to the deployment of distributed renewable resources (e.g. rooftop solar supply) and the need to mobilize the flexibility of consumers that are connected to the low-voltage grid. The pricing of electric power at distribution nodes follows directly from the theory of spot pricing of electricity. However, in contrast to linearized lossless models of transmission networks, an intuitive understanding of prices at the distribution level presents challenges due to voltage limits, reactive power flows and losses. In this paper we present three approaches towards understanding distribution locational marginal prices by decomposing them: (i) through a duality analysis of the problem formulated with a global power balance constraint, (ii) through a duality analysis of a second order cone program relaxation, and (iii) through an analysis of the impact of marginal losses on price. We discuss the relative strengths and weaknesses of each approach in terms of computation and physical intuition, and demonstrate the concepts on a 15-bus radial distribution network

Coupling Renewable Energy Supply with Deferrable Demand

The large-scale integration of renewable energy sources is advancing rapidly in numerous power systems. However, utilizing renewable resources at a bulk scale is hindered by the fact that these resources are neither controllable nor accurately predictable. Our analysis focuses on the cost of balancing power system operations in the presence of renewable resources and on the amount of operating reserves that is necessary for ensuring the reliable operation of the system. We also explore the extent to which demand-side flexibility can mitigate these impacts. We present a contract that couples the operations of renewable energy resources with deferrable loads that can shift a fixed amount of energy demand over a given time window. Various flexible energy consumption tasks can be characterized in this way, including electric vehicle charging or agricultural pumping. We use a two-stage stochastic unit commitment model for our analysis. The use of this model is justified by the fact that it is capable of quantifying the operating costs of the system and the amount of required capacity in order to face the increased uncertainty of daily operations. We present a dual decomposition algorithm for solving the model and various scenario selection algorithms for representing uncertainty. We present results for a reduced network of the California power system that consists of 124 generators, 225 buses and 375 lines. We fist validate the stochastic unit commitment policy that we derive from the stochastic optimization model by demonstrating that it outperforms deterministic unit commitment rules commonly used in practice. We demonstrate this superior performance for both a transmission-constrained as well as an unconstrained system for various types of uncertainty including network element failures as well as two levels of wind integration that roughly correspond to the 2012 and 2020 renewable energy integration targets of California. We then use the stochastic unit commitment model to quantify the impacts of coupling renewable energy supply with deferrable demand on operating costs and reserve requirements. We also demonstrate the superiority of coupling contracts to demand-side bidding in the day-ahead market which is due to the fact that demand bids fail to account for the inter-temporal dependency of deferrable demand

Intra-day Bidding Strategies for Storage Devices Using Deep Reinforcement Learning

peer reviewedThe problem faced by the operator of a storage device participating in a continuous intra-day (CID) market is addressed in this paper. The goal of the storage device operator is the maximization of the cumulative rewards received over the entire trading horizon, while taking into account operational constraints. The energy trading is modeled as a Partially Observable Markov Decision Process. An equivalent state representation and high-level actions are proposed in order to tackle the variable number of the existing orders in the order book. The problem is solved using deep reinforcement learning (RL). Preliminary results indicate that the agent converges to a policy that scores higher total revenues than the ``rolling intrinsic''