Online peak-aware energy scheduling with untrusted advice

This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competitive ratio as a function of the trust parameter when the advice is inaccurate, and consistency for competitive ratio when the advice is accurate. Since the competitive ratio is analyzed in two different regimes, we further investigate the Pareto optimality of the proposed algorithms. Our results show that the proposed deterministic algorithm is Pareto-optimal, in the sense that no other online deterministic algorithms can dominate the robustness and consistency of our algorithm. Furthermore, we show that the proposed randomized algorithm dominates the Pareto-optimal deterministic algorithm. Our large-scale empirical evaluations using real traces of energy demand, energy prices, and renewable energy generations highlight that the proposed algorithms outperform worst-case optimized algorithms and fully data-driven algorithms.

Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a majority of dishonest users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provided empirical evaluation in this paper 1 .

A holistic review on advanced bi-directional EV charging control algorithms

The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission. The fast development of EVs and charging facilities is driving the evolution of Internet of Vehicles (IoV) to Internet of Electric Vehicles (IoEV). IoEV benefits from both smart grid and Internet of Things (IoT) technologies which provide advanced bi-directional charging services and real-time data processing capability, respectively. The major design challenges of the IoEV charging control lie in the randomness of charging events and the mobility of EVs. In this article, we present a holistic review on advanced bi-directional EV charging control algorithms. For Grid-to-Vehicle (G2V), we introduce the charging control problem in two scenarios: 1) Operation of a single charging station and 2) Operation of multiple charging stations in coupled transportation and power networks. For Vehicle-to-Grid (V2G), we discuss how EVs can perform energy trading in the electricity market and provide ancillary services to the power grid. Besides, a case study is provided to illustrate the economic benefit of the joint optimization of routing and charging scheduling of multiple EVs in the IoEV. Last but not the least, we will highlight some open problems and future research directions of charging scheduling problems for IoEVs.

Characteristic profile

The rapid uptake of rooftop solar photovoltaic systems is introducing many challenges in the management of distribution networks, energy markets, and energy storage systems. Many of these problems can be alleviated with accurate short term solar power forecasts. However, forecasting the power output of distributed rooftop solar PV systems can be challenging, since many complex local factors can affect solar output. A common approach when forecasting such systems is to extract the daily seasonality from the time series using some form of seasonality model, and then forecast only the residuals that remain after seasonality extraction. In this work, we explore in detail the effectiveness of three commonly used seasonality models, and we propose a new one, called the "characteristic profile". We find that when seasonality models are integrated into the forecasting process, significant gains in forecast accuracy may be obtained - particularly for machine learning based approaches, which have a reduction in forecast error of 5-25%. Among the seasonality models, the characteristic profile demonstrates the highest forecast accuracy, resulting in reductions in forecast error of 8% or more compared to forecasting models that do not take seasonality into account. The benefits of this approach are particularly pronounced when forecasting solar PV systems that are curtailed, suffer from local shading, or consist of multiple sets of panels having different orientations and tilts. Our results are demonstrated on a high resolution dataset obtained from 258 sites in Western Australia over the course of a full year.

Data analytics for cybersecurity enhancement of transformer protection

Electric power substations are experiencing an accelerated pace of digital transformation including the deployment of LAN-based IEC 61850 communication protocols that facilitate accessibility to substation data while also increasing remote access points and exposure to complex cyberattacks. In this environment, machine learning algorithms will play a vital role in cyberattack detection and mitigation and natural questions arise as to the most effective models in the context of smart grid substations. This paper compares the performance of three autoencoder-based anomaly detection systems including linear, fully connected, and convolutional autoencoders, as well as long short-term memory (LSTM) neural network for cybersecurity enhancement of transformer protection. The simulation results indicated that the LSTM model outperforms the other models for detecting cyberattacks targeting asymmetrical fault data. The linear autoencoder, fully connected autoencoder and 1D CNN further outperform the LSTM model for detecting cyberattacks targeting the symmetrical fault data.

Towards online optimization for power grids

In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offline and online distributed optimization, showcasing the suitability of such optimization techniques for power grid applications.

Information batteries

Coping with the intermittency of renewable power is a fundamental challenge, with load shifting and grid-scale storage as key responses. We propose Information Batteries (IB), in which energy is stored in the form of information---specifically, the results of completed computational tasks. Information Batteries thus provide storage through speculative load shifting, anticipating computation that will be performed in the future. We take a distributed systems perspective, and evaluate the extent to which an IB storage system can be made practical through augmentation of compiler toolchains, key-value stores, and other important elements in modern hyper-scale compute. In particular, we implement one specific IB prototype by augmenting the Rust compiler to enable transparent function-level precomputation and caching. We evaluate the overheads this imposes, along with macro-level job prediction and power prediction. We also evaluate the space of operation for an IB system, to identify the best case efficiency of any IB system for a given power and compute regime.

Empiricism and collaboration on grid data analytics

This paper discusses the need for data-driven tools to manage modern electric grids, where planning and operational decisions increasingly require empirical data on various time scales. The advancement of such tools will hinge on deploying instrumentation to collect faster and more localized measurements, capitalizing on state-of-the-art software solutions to facilitate big-data workflows, and enabling open exchange of data and information with research collaborators.

Automated generation of models for demand side flexibility using machine learning

Flexibility in consumption and production provided by distributed energy resources (DERs) is a key to the integration of renewable energy sources into the energy system. However, even for identical DERs, the flexibility can vary widely, based on local constraints and circumstances. Therefore, handcrafting models can be labor-intensive and automating the generation of models could help increasing the volume of controllable flexibility in smart grids. Depending on the underlying mechanism for controlling demand side flexibility, there are various ways how an automation can be achieved. In this paper, we discuss fundamental concepts relevant to the automated generation of models for demand side flexibility, give an overview of different approaches, and point out fundamental differences. The main focus lies on model generation by means of machine learning techniques.

The next stage of green electricity labeling

The labeling of electricity is considered an important mechanism to differentiate renewable power generation and, thus, to incentivize the expansion of green energy. However, today's systems for documenting and trading green energy certificates suffer from multiple challenges. These could be addressed by a digital solution that holistically collects and processes production and consumption data. Blockchain-based architectures have repeatedly been suggested for this purpose since they can provide transparency and can likely be accepted by a broad group of stakeholders. Yet, there are significant scalability and privacy issues of a blockchain-based approach for storing and processing fine-grained production and consumption data. In this paper, we propose and discuss a potential solution that levers succinct cryptographic zero-knowledge proofs to balance the required level of transparency and privacy while at the same time providing a high degree of scalability.