Grid Services Research Articles

Optimization and Techno-Economic Analysis of a Microgrid for an Industry with Intermittent Grid Service using HOMER Software: A Case Study in Ladakh, India

Optimization and Techno-Economic Analysis of a Microgrid for an Industry with Intermittent Grid Service using HOMER Software: A Case Study in Ladakh, India

Active distribution networks or microgrids? Optimal design of resilient and flexible distribution grids with energy service provision

Ensuring the continuity of service in electricity distribution grids at minimum costs is a key challenge in many countries, as extreme weather events and fluctuating renewable energy increase. New grid concepts such as active distribution networks with distributed energy resources, or microgrids that can operate in islanded mode, offer opportunities to improve the reliability and resilience of distribution grids. This paper presents an active distribution network design optimization with the option to transition into a microgrid, quantifying reliability and resilience improvements, and considering faults within the network as well as unexpected islanding events, which require fast-ramping distributed energy resources. Flexibility, both of loads and distributed energy resources, is used to provide energy services to the surrounding grid with a detailed and general model. Solving the resulting mixed-integer linear program with Column and Constraint Generation and Surrogate Absolute Value Lagrangian Relaxation decreases the computational time significantly. Case studies and sensitivity analyses provide general results for the reliability and resilience targets entailing the optimum transition to microgrids, and quantify the contribution of energy service provision to reduce overall costs.

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption.

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.

A methodological framework for determining an optimal coastal protection strategy against storm surges and sea level rise

Interdependent critical infrastructures in coastal regions, including transportation, electrical grid, and emergency services, are continually threatened by storm-induced flooding. This has been demonstrated a number of times, most recently by hurricanes such as Harvey and Maria, as well as Sandy and Katrina. The need to protect these infrastructures with robust protection mechanisms is critical for our continued existence along the world’s coastlines. Planning these protections is non-trivial given the rare-event nature of strong storms and climate change manifested through sea level rise. This article proposes a framework for a methodology that combines multiple computational models, stakeholder interviews, and optimization to find an optimal protective strategy over time for critical coastal infrastructure while being constrained by budgetary considerations.

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Abstract Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

Increasing DER integration through discrete intraday settlements

Deployment of distributed energy resources (DERs) is accelerating across the United States. These assets have the potential to provide cost-effective grid services and bulk power while supporting a low-cost energy transition to carbon-free electricity. Full integration of these resources into the bulk power system remains a barrier to unlocking the potential value of these assets. The regional wholesale energy markets, originally designed to integrate centralized, dispatchable power plants, must be reformed to adapt to the changing potential resource mix. The current day-ahead and real-time system is poorly designed for DERs. Day-ahead DER forecast errors create exposure to real-time price variations, as resources committed in the day-ahead market are required to rebalance in real-time, challenging the business model for DERs. New intraday markets with multiple discrete intermediate auctions between the day-ahead market and real-time market are needed to enable proper integration of DERs and efficient mitigation of forecast uncertainty risk. These markets must incorporate financially-binding commitments, closer-to-dispatch price signals, and adequate liquidity to ensure competition. Intermediate auctions should be built on top of existing intraday processes in each regional market, incorporating the principles of the existing market structures and leveraging the benefits of the centralized dispatch systems. Regional transmission organization (RTOs) and Independent System Operators (ISOs) should pursue and implement intermediate auctions to support fair, open, and efficient markets in the U.S.

Electromobility and Flexibility Management on a Non-Interconnected Island

The increasing penetration of electrical vehicles (EVs), on the way to decarbonizing the transportation sector, presents several challenges and opportunities for the end users, the distribution grid, and the electricity markets. Uncontrollable EV charging may increase peak demand and impact the grid stability and reliability, especially in the case of non-interconnected microgrids such as the distribution grids of small islands. On the other hand, if EVs are considered as flexible loads and distributed storage, they may offer Vehicle to Grid (V2G) services and contribute to demand-side management through smart charging and discharging. In this work, we present a study on the penetration of EVs and the flexibility they may offer for services to the grid, using a genetic algorithm for optimum valley filling and peak shaving for the case of a non-interconnected island where the electricity demand is several times higher during the summer due to the influx of tourists. Test cases have been developed for various charging/discharging strategies and mobility patterns. Their results are discussed with respect to the current generating capacity of the island as well as the future case where part of the electricity demand will have to be met by renewable energy sources, such as photovoltaic plants, in order to minimize the island’s carbon footprint. Higher EV penetration, in the range of 20–25%, is enabled through smart charging strategies and V2G services, especially for load profiles with a large difference between the peak and low demands. However, the EV penetration and available flexibility is subject to the mobility needs and limited by the population and the size of the road network of the island itself rather than the grid needs and constraints. Limitations and challenges concerning efficient V2G services on a non-interconnected microgrid are identified. The results will be used in the design of a smart charging controller linked to the microgrid’s energy management system.

Distribution Market-Clearing and Pricing Considering Coordination of DSOs and ISO: An EPEC Approach

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

Adaptive Moth Flame Optimization based Load Shifting Technique for Demand Side Management in Smart Grid

The rising number of connected devices, energy management, and local generation facilities implies that the consumers have an increased responsibility to the energy sector. Demand-side management (DSM) techniques involve the consumer in assisting grid services, delay of capital expenses, and utility revenue streams through several behind the metre services. A variety of literature studies have presented many techniques for the implementation of DSM in smart grid systems. This study emphasizes the modelling of DSM utilizing a day-ahead load shifting approach as a minimization problem. The DSM is modelled as an optimization problem whose solution is attained through a nature-based adaptive moth flame (AMF) technique. The formulated work has been tested on three demand zones: residential, commercial, and industrial, with diverse controllable loads. A comparison of solutions based on reduced peak demand and operational costs is undertaken with the proposed AMF optimization algorithm. Finally, it is shown that the DSM technique based on the AMF technique exhibits better savings than the multi-agent and evolutionary approach in the residential and commercial sectors. However, the particle swarm technique proves a better alternative than the proposed technique in achieving cost savings.

Data reduction algorithm for correlated data in the smart grid

Smart grids are intelligent electrical networks that incorporate information and communication technology (ICT) to provide data services for the power grid. In this paper, the ICT requirements for monitoring and control of the neighbourhood area network level of the smart grid, with particular emphasis on making the ICT infrastructure energy efficient, are analysed. One approach to provide energy efficiency in the communication system is to develop a data reduction algorithm to reduce the volume of data prior to transmission. Thus, a data compression technique called DRACO (data reduction algorithm for correlated data) that shows a reasonable compression ratio while using network resources efficiently is designed and developed. DRACO can be applied to data with a high data sampling rate, and can transmit the essential information with compression ratios of 70%–99%. The results of applying DRACO on real data collected by devices located in the University of Manchester campus are discussed, followed by the evaluation and validation of DRACO by comparing it with other available techniques. Finally, it is concluded that DRACO is suitable for smart grid applications since it optimizes the network resource consumption and reduces the communication energy cost while maintaining the integrity and quality of data.

Scalable identification and control of residential heat pumps: A minimal hardware approach

While model predictive control (MPC) is a widely studied tool for integrating residential heat pumps with the smart grid, modeling difficulties and hardware requirements are significant barriers to adoption. In response, we present a nonintrusive plug-and-play methodology for model identification and model predictive control using only two increasingly popular smart-home devices: a smart thermostat and smart electricity meter, which makes the overall approach highly scalable. However, this minimal hardware approach requires new methods to overcome modeling challenges due to the lack of data diversity. A data-driven heat pump power model is identified without the need for submetering by using energy disaggregation. By incorporating the heat pump control data from the smart thermostat, we provide implicit load classification data to the model, substantially simplifying the signal decomposition task. Building and heat pump model parameters are then identified by a novel algorithm that combats overfitting caused by limited state excitation. Finally, we show the advantage of this scalable approach in an aggregate model predictive controller enabled by the communication ability provided by the smart thermostat. Results show that this aggregate control approach can provide improved grid services, such as reduced peak demand, while at the same time reducing energy consumption and improving thermal comfort. The proposed method has the potential to greatly lower the barrier for residential energy aggregation.

State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin

Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.

An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands

Due to natural variability and uncertainty, the ever-increasing penetration of solar generation in Hawaii presents challenges to power grid operators to maintain reliable system operation. Demand response (DR) has the potential to be a cost-effective tool for Hawaii to reach its aggressive renewable energy goals while maintaining the reliability of power grids. The Hawaii Public Utilities Commission has approved the Hawaiian Electric Company’s revised portfolio of DR programs. The companies have released a grid services purchase agreement and subscribed an initial tranche of load into their DR programs. This paper presents innovative analytical methods and comprehensive economic assessment for distributed photovoltaics (PV) paired with battery energy storage systems (BESSs) for two new DR programs, including fast frequency response and capacity grid service. Optimal dispatch and sizing methods are proposed for the paired system considering different tariff schedules and PV compensation programs across five islands. It was found that while the best resource configuration and potential economic benefits vary with tariff structure, a BESS paired with PV can be optimally dispatched to generate multiple value streams simultaneously. Compensation from DR programs is an important value stream to help increase the cost-effectiveness of the integrated system.

Microgrid decision-making by public power utilities in the United States: A critical assessment of adoption and technological profiles

The paper provides a critical review of microgrid development in the U.S., with an emphasis on the rationales for adoption and the technology configurations that are evident in large U.S. public power utilities. To do so, this study outlines specific historical and contemporary developments for the technology. It then tests deployment objectives from the literature for resonance with the adoption logic of large public power utilities that have community microgrids. Four conceptual models are identified, representing differing rationale for adoption and technology profiles. Broadly, this research finds that public power utilities with microgrid technologies have taken an incremental design approach that has not typically started with an explicit plan to island the system. In evaluating microgrid designs, the utilities leverage existing assets and respond to community priorities. The study also highlights the critical role of learning as a utility objective and as justification for technology investments. Clean/renewable policies play a more significant role than microgrid-specific policies for the public power utilities that are studied. Additionally, the study finds that two market structures factor in how microgrid deployment is shaped, namely market rules that allow users to avoid costs through load management; and market rules that facilitate the sale of energy and grid services in multilateral markets Finally, the study indicates that microgrid technology is currently reframing how distributed energy resources are conceived and interact with greater system-related value. Future research could explore additional types of business models to shed light on further values in existing circumstances and across multiple scales.

Design, thermodynamic, and wind assessments of a compressed air energy storage (CAES) integrated with two adjacent wind farms: A case study at Abhar and Kahak sites, Iran

Wind speed fluctuation at wind farms leads to intermittent and unstable power generation with diverse amplitudes and frequencies. Compressed air energy storage (CAES) is an energy storage technology which not only copes with the stochastic power output of wind farms, but it also assists in peak shaving and provision of other ancillary grid services. In this paper, a CAES facility is proposed for two adjacent wind farms, Abhar and Kahak sites in Iran, with a total nominal power of 162.5 MW. To assess site peak profiles and storage potential, annual thermodynamic and wind assessments are carried out predominantly for the three critical months of the year requiring mitigation of electricity scarcity (July, August, and September). Results indicate that the sustained wind speed in July at both the Abhar and Kahak sites is higher than the other two critical months. Around 93, 74 and 60 MW stored power in the CAES facility are added to the grid during 5 h of peak demand in July, August and September months with round trip efficiencies of 52, 47, and 43%, respectively.

A polymorphic heterogeneous security architecture for edge-enabled smart grids

The emergence of edge computing alleviates the network congestion, delay, and packet loss of cloud computing enabled smart grid architecture in big data scenarios, greatly improving the operation efficiency and communication quality of the smart grid. However, edge computing architecture also brings great security risks to the smart grid. This paper proposes a Polymorphic Heterogeneous Security Architecture (PHSA) for edge enabled smart grid, to assure the key services in the smart grid. Based on the dynamic heterogeneous redundancy architecture, the proposed architecture introduces intelligent scheduling of executors, intelligent arbitration of multi-state service response, cleaning and reconstruction of executors, and attack surface management. We establish a closed-loop core control process of decision and processing, adjustment and scheduling, cleaning reconstruction, migration, and recovery. Furthermore, a dynamic scheduling algorithm based on executor credit and heterogeneity is proposed. The simulation results show that, compared with the traditional smart grid security architecture, the PHSA has better defense ability and system reliability, significantly improving the security of edge enabled smart grid system. Besides, compared with the random scheduling strategy, the proposed dynamic executor scheduling algorithm can further improve the security of the system.

Low carbon policy and market mechanisms to enable carbon capture and storage and decarbonisation in Australia

The scale of decarbonisation required to meet global emission reduction targets necessitates every abatement technology available be adequately explored. Carbon capture and storage (CCS) allows decarbonisation for sectors that are otherwise technically difficult and expensive to transition. This analysis used a levelised cost of electricity (LCOE) approach to identify optimal market-based mechanisms that would provide necessary funding support for CCS deployment. The results of that analysis were presented to energy policy stakeholders to determine their amenability. Four mechanisms were investigated; capital subsidies, regulated requirements for increased grid services from all generators, a partial capacity and inertia market with emission constraints and a clean energy certificate scheme. As a means for encouraging CCS, subsidies were favoured by stakeholders. The financial analysis suggested this policy could assist CCS, although was not the optimal choice due to its potential market distortion. A carbon-constrained capacity and inertia market was the preferred pathway for the deployment of CCS technologies from an investment viewpoint. This market model encourages a range of other low-emission technologies, including large-scale batteries and pumped-hydro energy storage. Further modelling is required to determine the overall system and abatement costs involved in the implementation of a partial carbon-constrained capacity market.

Deep Reinforcement Learning-Based Service-Oriented Resource Allocation in Smart Grids

Resource allocation has a direct and profound impact on the performance of resource-limited smart grids with diversified services that need to be timely processed. In this paper, we investigate a joint communication, computing, and caching resource allocation problem with distinct delay requirement of services in smart grids. This paper aims to optimize the long-term system utility based on reward and loss function. Considering the unknown dynamic environment as well as the huge state and action space in smart grids, a deep reinforcement learning algorithm based on the polling method is exploited to learn the policy by interacting with the environment. Specifically, the edge nodes (ENs) act as agents to enable the services to schedule resources appropriately. Then, the agents that are allocated based on the service requirements are queried according to the polling mechanism and the well-designed reward function is utilized to update the strategy. Extensive simulation results show that the proposed algorithm outperforms three known baseline schemes in terms of network performance with decision results. Besides, in the face of a large number of services in the smart grids, the proposed system still surpasses that of existing several baseline schemes, especially in the improvement of cache hit rate and the decrease of computing delay.

Cloud Electricity Storage for Multi-Service Battery Operation: Case of RTE

Problem definition: We study a cloud storage operator that provides shared storage service for electricity end-users using the residual part of a multi-service grid-scale battery primarily used for high-priority grid services. We design an optimal product with regard to pricing and customer portfolio. Academic/practical relevance: A framework and solution approach for assessing and operating such multi-service battery operations with stochastic services and different priority levels is an open problem. Methodology: We model the problem as a two-stage stochastic optimization between high-priority stochastic grid services and low-priority cloud storage for stochastic end-users. We also propose operational metrics of multiplexing gain and probability of blocking to assess the operation of the multi-service multi-user battery. To address the computational challenge of solving the stochastic optimization with a large number of end-users, we propose effective capacity as a convex approximation that allows an analytical solution. We then provide an empirical analysis based on real grid congestion data from RTE France and a large dataset of end-users' electricity consumption in California. Results: Our empirical analysis shows (i) our proposed effective capacity is a close approximation, (ii) battery operation and profit are sensitive to the cost of external resources, number of end-users, and the leasing price of the battery, and (iii) with only a slight discount of the leasing price (~1%), the profit of the third party from a stochastic residual battery can be the same as that from a deterministic one. Managerial implications: Cloud storage as a low-priority service can profitably exist alongside other high-priority battery services, making integration of more storage in the grid economically viable and allowing larger intermittent renewables, a key path towards reduced carbon emissions.

A case study: Grid services for satellite image processing

Satellite image processing is an intricate task that requires vast computation and data processing, which cannotbe handled by a single computer. Furthermore, the processing of the massive amount of data accumulated bythe satellite is a huge challenge for the end user. Hence, grid computing is the essential platform to provide highcomputing performance at the user end. This article reviews the grid services used for satellite image processingand significant data processing.