Smart Grid Pilot Research Articles

Scalability and Replicability Analysis in Smart Grid Demonstration Projects: Lessons Learned and Future Needs

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects.

Optimal Wireless Technology Selection Approach for Sustainable Indian Smart Grid

The smart grid is playing a game-changing role in achieving clean and green energy, infrastructure, and cities, which are all part of the sustainable development goals. The significance of communication infrastructure in the reliable design and operation of the smart grid is well recognized, notably for renewable integration, facilitating distributed energy resources and storage, demand response, and energy efficiency. Since choosing the optimal communication technology is a strategic decision, the problem needs careful investigation, taking into account realistic data traffic estimates to fulfill the communication needs of the applications envisaged. Even though a vast array of technologies with diverse capabilities is available to meet such communication needs, choosing the optimal wireless technology for a smart grid project remains a difficult challenge. In this context, to achieve and maximize the benefits of the smart grid and its applications, a systematic and efficient approach is necessary. This study proposes a data-driven decision-making approach for evaluating the capabilities of viable wireless technology options and selecting the most suitable option for the smart grid project at the design phase. The suggested approach and the decision-support tool were developed using a cost-function-based optimization technique. A case study of Siliguri city Indian smart grid pilot is discussed to validate the potential and aptness of the presented approach and suggest better technology alternatives as replacements. Being field data-driven, the presented optimization approach is simple, customizable, strategic, and re-usable with practical efficacy to assist decision-making.

Aging aware adaptive control of Li-ion battery energy storage system for flexibility services provision

Battery energy storage systems (BESSs) play a major role as flexible energy resource (FER) in active network management (ANM) schemes by bridging gaps between non-concurrent renewable energy sources (RES)-based power generation and demand in the medium-voltage (MV) and low-voltage (LV) electricity distribution networks. However, Lithium-ion battery energy storage systems (Li-ion BESS) are prone to aging resulting in decreasing performance, particularly its reduced peak power output and capacity. BESS controllers when employed for providing technical ancillary i.e. flexibility services to distribution (e.g. through ANM) or transmission networks must be aware of changing battery characteristics due to aging. Particularly of importance is BESSs' peak power changes aiding in protection of the Li-ion BESS by restricting its operation limits of it for safety reasons and improving its lifetime in the long run. In this paper, firstly an architecture for ANM scheme is designed considering Li-ion BESSs as one of the FERs in an existing smart grid pilot (Sundom Smart Grid, SSG) in Vaasa, Finland. Further, Li-ion BESS controllers are designed to be adaptive in nature to include its aging characteristics, i.e. tracking the changing peak power as the aging parameter, when utilised for ANM operation in the power grid. Peak power capability of the Li-ion nickel‑manganese‑cobalt (NMC) chemistry-based battery cell has been calculated with the experimental data gathered from accelerated aging tests performed in the laboratory. Impact of such aging aware and adaptive Li-ion BESS controllers on the flexibility services provision for power system operators needs will be analysed by means of real-time simulation studies in an existing SSG pilot.

Plug-and-Play Algorithms for the Efficient Coordination of Active Distribution Grids

The tremendous complexity of modern distribution systems calls for alternative coordination architectures, supported by smart, self-adaptable, and, to a large degree, environment-agnostic algorithms. In this article, we discuss decentralized and distributed coordination architectures for the operation of active distribution grids aiming at effectively coping with their complexity. We present relevant methods and algorithms under the framework of multiagent systems (MASs) and decentralized decision-making associated with handling different parts of the optimal grid operation. The decision-making models are based on distributed optimization algorithms using consensus/gossip models, bioinspired algorithms from the field of population dynamics, and a method for decomposing the power-flow model. The developed techniques aim at matching production with demand in microgrids, settling the short-term energy imbalances at the distribution grid level, mitigating voltage deviations, and resolving distribution grid congestions in real-time operation. The algorithms are implemented as MAS-based software platforms, able to aggregate diverse DG units and flexible loads. Results are provided from the theoretical simulation-based models and demonstrations of the operational techniques in actual pilot sites. The applied implementations have been performed in a smart grid pilot site, for which MAS platforms have been developed and tested.

Modelling Barriers for Smart Grid Technology Acceptance in India

The numerous challenges faced by the power sector in India, like high transmission and distribution loss, power theft, frequent power outages and under billing, have impelled to adopt new technologies like smart grid to make the power grid more efficient and secure, leading to the need for interdisciplinary research in the area of power systems and management. An ambitious target of the Government of India to produce 175 GW of renewable energy by 2022 has created a drive towards implementing smart grid technology in India. Though the smart grid seems to be a promising solution, it requires considerable investment, infrastructure development and social acceptance by the consumers. Investigation of smart grid pilot projects in India clearly indicates a lack of consumer involvement and poor infrastructure development. This research aims to identify factors that can be considered barriers to smart grid technology acceptance in India and develop a hierarchical model. The present work has implemented a systematic and scientific approach that includes content analysis, exploratory factor analysis (EFA), total interpretive structural modelling (TISM) and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) to develop a hierarchical model for smart grid technology acceptance. The result highlights the importance of consumer awareness and various factors affecting the consumer’s participation in the successful delivery of smart grid technology. It will undoubtedly help policymakers frame the long-term strategies for implementing smart grid technology, especially in a developing country like India. This paper’s contribution from a developing country perspective, made by findings on prioritising barriers, can help managers and policymakers plan a strategy for implementing the smart grid. The findings will undoubtedly help understand the significant factors for smart grid development. They can also be used as a reference for other developing countries with similar economic and social conditions that are planning to promote smart grids.

Self-Healing Dilemmas in Distributed Systems: Fault Correction vs. Fault Tolerance

Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process. In the case of a slow process without fault, fault correction is undesirable as it can trigger new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults can make systems underperforming. Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health applications. This paper contributes a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase. A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes. Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project. Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing at an early stage. Strikingly, the aggregation accuracy is well predicted as shown by high correlations and low root mean square errors.

Modelling battery energy storage systems for active network management—coordinated control design and validation

Control of battery energy storage systems (BESS) for active network management (ANM) should be done in coordinated way considering management of different BESS components like battery cells and inverter interface concurrently. In this paper, a detailed and accurate lithium-ion battery model has been used to design BESS controls, thereby allowing improved overall power system control design optimisation studies by simultaneously considering both component and system-level aspects. This model is utilised to develop a multi-objective ANM scheme (a) to enhance utilisation of wind power generation locally by means of active power (P)- control of BESSs; (b) to utilise distributed energy resources (i.e. BESS and wind turbine generators) to maintain system voltage within the limits of grid code requirements by reactive power/voltage (QU)- and active power/voltage (PU)- controls. BESS control strategies to implement the ANM scheme, are designed and validated through real-time simulation in an existing smart grid pilot, Sundom Smart Grid (SSG), in Vaasa, Finland.

Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources

The article establishes a three-state energy (TSE) model for flexible energy sources (FESs) connected to smart grids. The article proposes a unified time-scale (UTS) coordinated primary voltage control framework and a UTS coordinated primary voltage controller for voltage control of smart grids containing a high proportion of FESs. To mitigate uncoordinated voltage, the proposed control framework integrates traditional secondary and primary voltage control into a UTS. The article proposes an expandable deep width learning (EDWL) for the proposed controller. The proposed controller applies the EDWL for predictive control; the proposed controller outputs the reactive power reference value of each TSE unit in smart grids with the real-time voltages of smart grids pilot buses. The proposed algorithm is numerically simulated with the proportional-integral-derivative (PID) algorithm and deep neural networks (DNNs) in IEEE 118-bus, 300-bus, 1354-bus, and 2383-bus systems. The simulation results show that the proposed framework and controller can quickly and accurately control the grid voltage, and verify the feasibility and effectiveness of the proposed approach. The integral of squared error control performance index is 0.47% smaller than the PID and 0.06% smaller than DNNs.

The precursors of acceptance for a prosumer-led transition to a future smart grid

ABSTRACT Centralised electricity grids around the world are transitioning towards greater renewable distributed storage, and pivotal to transition is the relationship between prosumers and utilities. Evidence of this changing relationship is observed through a three-year (2016–2019) Australian smart grid pilot. This study investigates the precursors of technology acceptance of smart batteries, because a lack of acceptance presents risks to implementation. We draw on: scholarly literature, government documents, interviews and observations of the pilot implementation for a qualitative, exploratory, longitudinal and thematic analysis. We outline four findings: Firstly, we suggest a reframing of the influence of agency in IoT-systems: The concept of ‘behaviour’ might be conditionally substituted for ‘agency’ in the case of smart grids. Secondly, we reconfirm prosumers require different agency-focused engagement than is currently offered if private investment in batteries is to be encouraged. Thirdly, prosumers do not always desire increased agency – there is a trade-off between agency and trust under the duress of high system complexity. Finally, we extend a variation of the technology acceptance model (known as RITAM) to include Perceived Trust, Agency and Complexity in relation to smart grids, and note these acceptance precursors pose both a risk and an enhancer to smart grid implementation.

Smart grids and institutional change: Emerging contestations between organisations over smart energy transitions

Smart grids are promoted as promising pathways for dealing with new grid challenges that have arisen by the introduction of renewable energies. In Germany, increasing shares of volatile renewables have led to a growing number of smart grid pilot projects and related regulatory and market developments. Even though, much has been done to develop the smart grid, significant difficulties remain, in particular, the re-negotiation of new roles and responsibilities of the organisational actors involved. From a sociological perspective, these shifts imply changes to current institutional arrangements within energy systems. Drawing on new organisational institutionalism and a qualitative analysis of German smart grid developments, this paper sheds light on organisations’ differing practices aimed at creating, maintaining and disrupting institutions (i.e. institutional work). First, we show how organisations’ existing roles, rules, norms, and beliefs are being challenged (or not) through the rise of smart grid technologies and what contestations have arisen within the smart grid field. Second, we analyse how organisations attempt to influence institutional changes and identify five different forms of institutional work conducted by actors in the German smart grid field. The paper demonstrates how organisations within smart grid developments attempt to reconfigure institutional arrangements in diverging or even contradictory ways. The paper reveals how the re-institutionalisation processes related to smart grids require fundamental changes in the common meaning system. Implementing these changes will remain a challenge if actors try to maintain existing institutional arrangements.

Designing for justice in electricity systems: A comparison of smart grid experiments in the Netherlands

In future urban energy systems, smart grid systems will be crucial for the integration of renewable energy. However, their deployment has moral implications, for example regarding data privacy, user autonomy, or distribution of responsibilities. ‘Energy justice’ is one of the most comprehensive frameworks to address these implications, but remains limited regarding smart grids, and regarding concrete guidelines for designers and policymakers. In this paper, we fill this gap by answering the following research question: How do design choices in smart grid projects impact energy justice? Thereby, four smart grid pilot projects are evaluated in a comparative qualitative case study research design. Data was collected through semi-structured interviews and a content analysis. Our findings contribute to the energy justice literature with insights regarding the design for distributive, recognition, and procedural justice. They underscore the importance of fairness in data governance, participatory design, user control and autonomy, technology inclusiveness, and the design for expansion and replication. Future research should explore the feasibility to govern smart grids as commons and the relationship between trust and perceptions of justice. We conclude with policy recommendations for funding future smart grid experiments and for facilitating the implementation of storage through electricity sector regulation.

Transition to decentralised electricity storage: The complexities of consumer decision-making and cost-benefit analyses

An industry-supported smart grid pilot on Bruny Island, Australia (2016–2019) trialled the cost-effectiveness and management of consumer-owned energy storage for a potential future grid. Understanding consumer decision-making is pivotal to smart-battery uptake and depends on a relevant value proposition. An in-depth, exploratory case study of a cost-benefit analysis (CBA) is presented, with the CBA found to be favourable, due to low ongoing costs and the pilot project subsidy. However, some pilot participants remained dissatisfied despite a beneficial CBA. This demonstrates additional logics outside of the CBA methodology. One example explored was the value of battery backup for reliability. Policy learnings include the fact that battery backup is an important value for prosumers in rural contexts. However, battery backup might have value and relevance in urban contexts where energy self-consumption can be maximised (for example, with the addition of electric vehicles). Finally, policy makers might consider smart-battery schemes to provide detailed, transparent, CBA-like data for energy consumers to enable transparency, trust, and more accessible information to assist decision-making processes.

The Social Appraisal of Techno-Experiments: Whirlpools and Mosaics of Smart Urbanism

ABSTRACT Technology-driven experiments—techno-experiments—have become a central mode of spatial intervention in a paradigm of smart growth. They are often considered a manifestation of a techno-managerial approach to governance, built upon the increasing influence of IT corporations on urban politics. Yet, there is little evidence indicating how these interests articulate techno-experiments and shape their legacies over the long run. This paper questions the varied politics of techno-experiments by comparing four projects in Stockholm and Amsterdam: two smart energy grid pilots and two online community-based platforms. Mobilizing the notion of the “social appraisal of technology,” it argues that techno-experiments can take different forms depending on how the role of digital technology is defined and negotiated by actors throughout the process of experimentation. The paper empirically shows that experiments can evolve in two main ways, defined as “whirlpools” and “mosaics.” As whirlpools, they upscale self-referentially; as mosaics, they instead extend into a set of scattered spin-offs. The key factors producing such outcomes, these cases show, are the form of partnership established at the outset of techno-experiments, and the ability of research funding and governmental agencies to steer projects as they develop.

Knowledge sharing in smart grid pilot projects

The major role that the electrification of the energy system is projected to play in the transition to a sustainable economy increases the pressure on the electricity grid and thereby creates a demand for the implementation of smart grid technologies. The interdependencies present in the electricity system require, and have led to, the wide-scale adoption of pilot projects to develop knowledge about the application of these technologies. While the knowledge sharing that stems from these projects is one of the justifications for subsidising these projects, it has remained largely a black box. Based on the analysis of interviews with the project leaders of sixteen smart grid pilot projects, complementary secondary data sources and a survey, we studied knowledge sharing at four levels: intra-organisational, intra-project, inter-project and project-external knowledge sharing. At each level we observed specific sublevels, mechanisms and barriers, resulting in complex knowledge sharing dynamics. While the projects succeeded in developing knowledge, knowledge sharing between projects run by different consortium partners rarely occurred and project-external knowledge sharing was primarily unidirectional and involved generic knowledge. Based on the results a set of recommendations was developed that can stimulate the knowledge sharing and thereby increase the value generated by these projects.

Predictive Voltage Control: Empowering Domestic Customers With a Key Role in the Active Management of LV Networks

In order to avoid voltage problems derived from the connection of large amounts of renewable-based generation to the electrical distribution system, new advanced tools need to be developed that are able to exploit the presence of Distributed Energy Resources (DER). This paper describes the approach proposed for a predictive voltage control algorithm to be used in Low Voltage (LV) distribution networks in order to make use of available flexibilities from domestic consumers via their Home Energy Management System (HEMS) and more traditional resources from the Distribution System Operator (DSO), such as transformers with On-Load Tap Changer (OLTC) and storage devices. The proposed algorithm—the Low Voltage Control (LVC)—is detailed in this paper. The algorithm was tested through simulation using a real Portuguese LV network and real consumption and generation data, in order to evaluate its performance in preparation for a field-trial validation in a Portuguese smart grids pilot.

ANALISIS PERBANDINGAN UNJUK KERJA WIND TURBINE TSD-500 DAN GH – 0.5K DI PILOT SMART GRID TEKNIK ELEKTRO UNIVERSITAS UDAYANA

Wind power generation is a power plant that converts kinetic energy into electrical energy by utilizing wind as its energy source. The Smart Grid Pilot Project in Microgrid, Udayana University's Electrical Engineering Study Program has a wind power plant for research. The wind power plants in the Smart Grid Pilot Project in Microgrid Udayana University's Electrical Engineering Study Program totaled 10 turbines with rated power of 500 Watt each, from 10 wind turbines there are 8 wind turbines with TSD-500 models made in Indonesia and 2 wind turbines with GH-0.5K models made in China. The data logger contained in the Pilot Smart Grind in Electrical Engineering, Udayana University, logs 10 wind turbines at a time, so the output power of each wind turbine is unknown. Performance analysis of wind turbine TDS-500 and GH-0.5K using a measuring instrument based on the ATmega 328 microcontroller so that it can find out which wind turbine is larger which results in better power output and performance. The results of this study indicate that the TSD-500 wind turbine performance is better than the GH-0.5K wind turbine.

Large scale smart meter data assessment for energy benchmarking and occupant behaviour profile development

This paper will present objectives and first results of the research project entitled “Large Scale Smart Meter Data Assessment for Energy Benchmarking and Occupant Behaviour Profile Development of Building Clusters,” implemented in the geographical scope of Hungary. The project seeks to utilize a new and unique opportunity for accessing and processing an enormous dataset collected by smart meters. Recently in Hungary, nearly 10 000 buildings have been equipped with smart meters within the “Central Smart Grid Pilot Project”. By means of advanced data analysis techniques, consumption trends and motivations of building users are being investigated. The aims are to help building designers and engineers design more energy efficient buildings at lower investment costs by avoiding system oversizing, and to obtain better knowledge about hourly, daily and monthly energy consumption trends. Furthermore, standard net demand values for normative energy calculations can be updated and specified more precisely since consumption habits change with time and depend on the region.

A Comparison of Households’ Energy Balance in Residential Smart Grid Pilots in the Netherlands

This paper presents an analysis that frames the impact of various smart grid technologies, with an objective to provide a transparent framework for residential smart grid demonstration projects based on predefined and clearly formulated key performance indicators. The analysis inspects measured energy data of 217 households from three smart grid pilot projects in the Netherlands and a public dataset with smart meter data from 70 households as a reference. The datasets were evaluated for one year and compared to provide insights on technologies and other differences based on seven key performance indicators, giving a comprehensive overview: monthly electricity consumption (100–600 kWh) and production (4–200 kWh); annually imported (3.1–4.5 MWh) and exported (0.2–1 MWh) electricity; residual load; peak of imported (4.8–6.8 kW) and exported (0.3–2.2 kW) electricity; import simultaneity (20–70.5%); feed in simultaneity (75–89%); self-sufficiency (18–20%); and self-consumption (50–70%). It was found that the electrification of heating systems in buildings by using heat pumps leads to an increase of annual electricity consumption and peak loads of approximately 30% compared to the average Dutch households without heat pumps. Moreover, these peaks have a high degree of simultaneity. To increase both the self-sufficiency and self-consumption of households, further investigations will be required to optimize smart grid systems.

Holarchic structures for decentralized deep learning: a performance analysis

Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.

Smart Appliances for Efficient Integration of Solar Energy: A Dutch Case Study of a Residential Smart Grid Pilot

This paper analyzes the use patterns of a residential smart grid pilot in the Netherlands, called PowerMatching City. The analysis is based on detailed monitoring data measured at 5-min intervals for the year 2012, originating from this pilot which was realized in 2007 in Groningen, Netherlands. In this pilot, smart appliances, heat pumps, micro-combined heat and power (µ-CHP), and solar photovoltaic (PV) systems have been installed to evaluate their efficiency, their ability to reduce peak electricity purchase, and their effects on self-sufficiency and on the local use of solar electricity. As a result of the evaluation, diverse yearly and weekly indicators have been determined, such as electricity purchase and delivery, solar production, flexible generation, and load. Depending on the household configuration, up to 40% of self-sufficiency is achieved on an annual average basis, and 14.4% of the total consumption were flexible. In general, we can conclude that micro-CHP contributed to keep purchase from the grid relatively constant throughout the seasons. Adding to that, smart appliances significantly contributed to load shifting in peak times. It is recommended that similar evaluations will be conducted in other smart grid pilots to statistically enhance insights in the functioning of residential smart grids.