Smart Grid Energy Research Articles

Energy forecasting based on predictive data mining techniques in smart energy grids

Energy forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. In this paper we aim to assess the performance of a forecasting model which is a weather-free model created using a database containing relevant information about past produced power data and data mining techniques. The idea of using a weather-free data-driven model is first to alleviate the dependence on weather data which, in some scenarios is difficult to obtain and second to reduce the computational effort. In this work, we aim first to evaluate the interplay between anomaly detection techniques and forecasting model accuracy. Secondly we will determine out of the three defined performance metrics, which one is the best for this particular application.

Distributed Machine Learning on Smart-Gateway Network toward Real-Time Smart-Grid Energy Management with Behavior Cognition

Real-time data analytics for smart-grid energy management is challenging with consideration of both occupant behavior profiles and energy profiles. This article proposes a distributed and networked machine-learning platform on smart-gateway-based smart-grid in residential buildings. It can analyze occupant behaviors, provide short-term load forecasting, and allocate renewable energy resources. First, occupant behavior profile is captured by real-time indoor positioning system with WiFi data analytics; and the energy profile is extracted by real-time meter system with electricity load data analytics. Then, the 24-hour occupant behavior profile and energy profile are fused with prediction using an online distributed machine-learning algorithm with real-time data update. Based on the forecasted occupant behavior profile and energy profile, solar energy source is allocated to reduce peak demand on the main electricity power-grid. The whole management flow can be operated on the distributed smart-gateway network with limited computational resources but with a supported general machine-learning engine. Experimental results on occupant behavior extraction show that the proposed algorithm can achieve 91.2% positioning accuracy within 3.64m. Moreover, 50× and 38× speed-up is obtained during data testing and training, respectively, when compared to traditional support vector machine (SVM) method. For short-term load forecasting, it is 14.83% more accurate when compared to SVM-based data analytics. Based on the predicted occupant behavior profile and energy profile, our proposed energy management system can achieve 19.66% more peak load reduction and 26.41% more cost saving as compared to the SVM-based method.

Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams

Smart grids equipped with bi-directional communication flow are expected to provide more sophisticated consumption monitoring and energy trading. However, the issues related to the security and privacy of consumption and trading data present serious challenges. In this paper we address the problem of providing transaction security in decentralized smart grid energy trading without reliance on trusted third parties. We have implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions. We conducted case studies to perform security analysis and performance evaluation within the context of the elicited security and privacy requirements.

LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic Reasoning

In the era of Big Data and IoT, successful systems have to be designed to discover, store, process, learn, analyse, and predict from a massive amount of data—in short, they have to behave intelligently. Despite the success of non-symbolic techniques such as deep learning, symbolic approaches to machine intelligence still have a role to play in order to achieve key properties such as observability, explainability, and accountability. In this paper we focus on logic programming (LP), and advocate its role as a provider of symbolic reasoning capabilities in IoT scenarios, suitably complementing non-symbolic ones. In particular, we show how its re-interpretation in terms of LPaaS (Logic Programming as a Service) can work as an enabling technology for distributed situated intelligence. A possible example of hybrid reasoning—where symbolic and non-symbolic techniques fruitfully combine to produce intelligent behaviour—is presented, demonstrating how LPaaS could work in a smart energy grid scenario.

Stochastic Games for the Smart Grid Energy Management With Prospect Prosumers

In this paper, the problem of the smart grid energy management under stochastic dynamics is investigated. In the considered model, at the demand side, it is assumed that customers can act as prosumers who own renewable energy sources and can both produce and consume energy. Due to the coupling between the prosumers’ decisions and the stochastic nature of renewable energy, the interaction among prosumers is formulated as a stochastic game, in which each prosumer seeks to maximize its payoff, in terms of revenues, by controlling its energy consumption and demand. In particular, the subjective behavior of prosumers is explicitly reflected into their payoff functions using the prospect theory, a powerful framework that allows modeling real-life human choices, rather than objective, user-agnostic decisions, as normative models do. For this prospect-based stochastic game, it is shown that there always exists a stationary Nash equilibrium where the prosumers’ trading policies in the equilibrium are independent of the time and their histories of the play. Moreover, to obtain one of such equilibrium policies, a novel distributed algorithm with no information sharing among prosumers is proposed and shown to converge to an $\epsilon$ -Nash equilibrium in which each prosumer is able to achieve its optimal payoff in an equilibrium up to a small additive error $\epsilon$ . On the other hand, at the supply side, the interaction between the utility company and the prosumers is formulated as an online optimization problem in which the utility company's goal is to learn its optimal energy allocation rules. For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori . Simulation results justify the convergence of the proposed algorithms and present new insights toward more efficient energy management in the smart grids.

Smart Village Load Planning Simulations in Support of Digital Energy Management for Off-grid Rural Community Microgrids

In isolated rural village electrification, renewable energy resources are often the only alternative to provide sustainable energy to economically deprived isolated rural communities. In support of current alternative energy system developments, engineers use smart village based computer model design approaches. This includes desktop computer simulation modelling for renewable energy systems and smartgrid energy management systems to plan and scope village energy projects for particular technology configurations. This approach also supports design site component option appraisals before physical installation at targeted pilot sites. Disaggregated demand load data from advanced metering infrastructure is however hardly available to assist with the technical planning and design optimization for planned rural energy systems at remote rural villages. This means that the logical demand load profiles for traditional rural villages have to be computer simulated. In this paper, we describe the basic principles around discrete time device disaggregated rural village electrical load profile simulations suitable for experimentation with smart microgrid design, economic optimization and critical demand response analysis. The engineering simulation model incorporates physical appliance energy ratings and device-use behaviour patterns as basis for synthesising disaggregated archetypal load profiles. The simulated disaggregated load category archetypes reflect realistic disaggregated energy consumption patterns for devices in typical isolated rural villages. Computer generated rural village load time-series datasets are output in formats suitable for exports and imports of demand load data directly into custom or commercial energy modelling software simulation platforms such as TRNSYS, HomerEnergy, EnergyPlan and EnergyPlus. The simulated rural village demand load data can thus be used to validate numerical simulation models for newly planned smart rural village energy systems, or experimentation with economic optimization and demand response for multi-priority load control in rural smart microgrid environments. Keywords: Cyber physical systems, demand response, disaggregated load profile, discrete time simulation, off-Grid energy systems, rural electrification, Smart rural village.

Exploiting data centres energy flexibility in smart cities: Business scenarios

In this paper, we have considered Data Centres (DCs) as computing facilities functioning at the crossroad of electrical, thermal and data networks and have defined optimisation techniques to exploit their energy flexibility. Our methods are leveraging on non-electrical cooling devices such as thermal storage and heat pumps for waste heat reuse and IT workload execution time shifting and spatial relocation in federated DCs. To trade energy flexibility we have defined an Energy Marketplace which allows DCs to act as active energy players integrated into the smart grid, contributing to smart city-level efficiency goals. Reinforcing this vision, we have proposed four innovative business scenarios that enable next generation smart Net-zero Energy DCs acting as energy prosumers at the interface with smart energy grids within smart city environments. Simulation experiments are conducted to determine the DCs potential electrical and thermal energy flexibility in meeting various network level goals and to assess the financial viability of the defined business scenarios. The results show that DCs have a significant amount of energy flexibility which may be shifted and traded to interested stakeholders thus allowing them to gain new revenue streams not foreseen before.

Materials Based on Antimony and Bismuth for Sodium Storage.

Sodium-ion batteries (SIBs) that efficiently store electricity into chemical energy have been extensively pursued because of their great potential for low-cost and large-scale stationary application such as smart grid and renewable energy. Successful deployment of SIBs requires efficient anode materials that could store Na+ ions via a reversible way at reasonable rates. Materials based on antimony and bismuth are capable of storing a high-concentration of Na+ ions via a reversible alloying reaction at suitable redox potentials, and thus have drawn substantial attention. However, these electrode materials are facing significant technical challenges, such as poor conductivity, multiple phase transformation, and severe volume swelling and shrinking, which make efficient materials design a necessity. In this review, we will give a latest overview of research progress in the design and application of electrode materials based on antimony and bismuth, and offer some value insights into their future development in sodium storage.

Research on the method of eliminating new energy in smart grid

Electric power system has great development in China, the smart grid system is more and more perfect, as the global energy problem gradually revealed, how in the smart grid system of recruiting energy dissipation has become a new research point, through the analysis of the current existing new energy development and given new energy of the main technology, puts forward two Suggestions, the first is to optimize new energy power generation forecasting, the second is give full consideration to the development of distributed power supply.

JouleMR: Towards Cost-Effective and Green-Aware Data Processing Frameworks

Interests have been growing in energy management of the cluster effectively in order to reduce the energy consumption as well as the electricity cost. Renewable energy and dynamic pricing schemes in smart grids are two major emerging trends in energy markets. However, current data processing frameworks are not aware of the efficiency of each joule consumed by the data center workloads in the context of these two major trends. In fact, not all joules are equal in the sense that the amount of work that can be done by a joule can vary significantly in data centers. Ignoring this fact leads to significant energy waste (by 25 percent of the total energy consumption in Hadoop YARN on a Facebook production trace according to our study). In this paper, we propose JouleMR , a cost-effective and green-aware data processing framework. Specifically, we investigate how to exploit such joule efficiency to maximize the benefits of renewable energy as well as dynamic pricing schemes for MapReduce framework. We develop job/task scheduling algorithms with a particular focus on the factors on joule efficiency in the data center, including the energy efficiency of MapReduce workloads, renewable energy supply, dynamic pricing and the battery usage. We further develop a simple yet effective performance-energy consumption model to guide our scheduling decisions. We have implemented JouleMR on top of Hadoop YARN. The experiments demonstrate the accuracy of our models, and the effectiveness of our cost-effective and green-aware optimizations outperform the state-of-the-art implementations over Hadoop YARN.

Agreement Technologies for Coordination in Smart Cities

Many challenges in today’s society can be tackled by distributed open systems. This is particularly true for domains that are commonly perceived under the umbrella of smart cities, such as intelligent transportation, smart energy grids, or participative governance. When designing computer applications for these domains, it is necessary to account for the fact that the elements of such systems, often called software agents, are usually made by different designers and act on behalf of particular stakeholders. Furthermore, it is unknown at design time when such agents will enter or leave the system, and what interests new agents will represent. To instil coordination in such systems is particularly demanding, as usually only part of them can be directly controlled at runtime. Agreement technologies refer to a sandbox of tools and mechanisms for the development of such open multiagent systems, which are based on the notion of agreement. In this paper, we argue that agreement technologies are a suitable means for achieving coordination in smart city domains, and back our claim through examples of several real-world applications.

Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV) generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM) technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC) maximum power point tracking (MPPT) technique that is based on proportional integral (PI) controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN), ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO) techniques and their performance are compared with existing models like back propagation (BP) forecasting model.

Electrochemical Properties of Lithium Based Liquid Metal Battery with Tin-Bismuth Cathode Materials

Reliance on diesel fuel in military operations is a weak point due to the challenge of transporting fuel through hostile regions. To reduce fuel consumption and emissions, military microgrids are now considered as a solution to address its needs for improved energy security and reliability of military bases. Particularly, in military microgrids, stationary energy storage system plays a critical role in guaranteeing the microgrids stability and reliability. Recently, research into liquid metal battery has been ramped up for applications in smart grid energy storage system due to the inherent advantages of long cycle life, simple fabrication process, and low cost of materials. D. J. Bradwell et al. have initially proposed magnesium-antimony (Mg‖Sb) liquid metal battery which exhibits 97%, 71%, and 69% of Coulombic, voltage, and energy efficiencies, respectively. However, Mg‖Sb chemistry is not consistent enough for practical use in smart grid energy storage system due to the high operating temperature of 700°C and low discharge voltage of ca. 0.21 V. To overcome such problems efficiently, lithium metal has recently attracted attention as alternative anode material because of its low melting point (181°C), low electronegativity, and the lowest solubility in molten salts. X. Ning et al. have reported lithium-bismuth (Li‖Bi) liquid metal battery operating at a temperature of 550°C. K. Wang et al. have reported lithium-antimony-lead (Li‖Sb-Pb) operating at a temperature of 450°C. However, lead can leave behind serious environmental and human health problems. In this study, we adopted eutectic tin-bismuth (Sn-Bi) alloy as cathode material because it is widely used for lead-free solder material in electronics due to its low melting point of 139°C. We have investigated the electrochemical properties (Coulombic, voltage, and energy efficiencies) of pure tin and bismuth as well as eutectic tin-bismuth alloy to evaluate the effect of adding bismuth into tin. After charging and discharging of lithium-tin-bismuth (Li‖Sn-Bi) liquid metal battery, microstructural analysis using scanning electron microscope and X-ray computational tomography was also carried out to investigate alloying/de-alloying reactions of lithium into tin and bismuth.

Zinc-Ion Inserted a-MnO2/Onion like Carbon (OLC) Nanorod Composite Cathode Materials for Aqueous Zn-Ion Battery

High-energy density lithium-ion batteries (LIBs) are ultimately the key drivers for portable electronic devices, hybrid electric vehicles and smart grid-energy storage systems [1]. However, the potential scarcity of lithium resources, which are used as cathode and organic electrolytes in batteries, as well as their low safety, will be a serious impediment to their large scale production. Thus, for the past decade, many researchers have been looking for alternative batteries such as Na+, K+ and Zn2+ [2]. Recently, an aqueous Zn-ion rechargeable battery (ZIB) has a tremendous exposure with in the research world market because of potential applications in large-scale energy storage. Owing to its various outstanding properties such as reasonable redox potential, high safety, low cost, most abundant and eco-friendly compared with the post lithium-ion batteries. Moreover, Zn metal as an anode delivers high gravimetric and volumetric capacities of 820 mAh g-1 and 5855 mAh cm-3 [3]. It is well known that the manganese oxide (MnO2)cathode material has been widely studied for lithium-ion batteries, sodium-ion batteries and newly discovered Zinc-ion batteries due to its excellent electrochemistry, high abundance and low cost. The previous explorations of the cathode materials such as manganese based oxides (a-b-g-d-MnO2 and todorokite-MnO2) were used for aqueous Zn-ion batteries [4, 5]. Among them, a-MnO2 is very attractive and has become the subject of great interest since it has a large [2×2] tunnel structure along its c-axis composed of four edge-sharing MnO6 octahedral units. However, a-MnO2 has subdued capacity fading during the initial 20 cycles, poor rate capabilities and inadequate cycling performance thus hinders its practical application of Zn-ion batteries. To solve this issue, the carbon based composite materials has been proposed [6]. Hence, for the first time in the present work, the Zn-ion inserted manganese oxide nano-rod composite (Zn1-xMnO2/OLC) cathode materials were evaluated for aqueous ZIBs. Different concentrations of Zn ion inserted MnO2 (Zn1-xMnO2) nano-rods were prepared by one step molten salt method. The Zn1-xMnO2/OLC composite cathode materials have been prepared by appropriate mixing of Zn1-xMnO2 and OLC using ultra-sonication and magnetic stirrer. The structural (XRD) and morphological (FE-SEM and TEM) reveals there are no phase changes after the insertion of Zn in to the host structure and below 50 nm nano-rod morphology. The electrochemistry was demonstrated using Zn-foil as the anode and the Zn1-xMnO2/OLC composite as the cathode. The 1M ZnSO4 with the addition of MnSO4 with different molar concentrations such as (0.1, 0.3 and 0.5) was used as an aqueous electrolyte. The electrochemistry of the fabricated cells was examined by cyclic voltammetry and galvanostatic cycling under a constant current density of 246 mAg-1 with the potential window of 1.0-1.8 V. The Zn1-xMnO2/OLC exhibits highly stable and better capacity retention (95%) compared to pure a-MnO2 cathode materials (55%). The enhanced cycling and capacity retention may be due to the OLC or Zn ion insertion in to the a-MnO2 structure. In this presentation, we will discuss in detail the physical and chemical properties of this new cathode material and their electrochemical performance in aqueous media. References Ammundsen, J. Paulsen, Adv. Mater., 2001, 943-956.Pasta, et al., Nature commun., 2014, 3007.Kaveevivitchai and A. Manthiram, J. Mater. Chem. A, 2013, 1-6.Pan, Y. Shao, etc.., Nature Energy, 2016, 1-7.H. Alfaruqi, J. Gim, etc., Electrochem. Commun. 2015, 121-125.Xu, B. Li, etc., Electrochim. Acta, 2014, 254-261.

Environmental assessment of Smart City Solutions using a coupled urban metabolism—life cycle impact assessment approach

The purpose of the study is to quantify the environmental performance of Smart City Solutions at urban system level and thus evaluate their contribution to develop environmentally sustainable urban systems. Further, the study illustrates how this quantification is conducted. The case city chosen in our modeling is Copenhagen, where seven Smart City Solutions are introduced: Green Roofs, Smart Windows, Pneumatic Waste Collection, Sensorized Waste Collection, Smart Water Meters, Greywater Recycling, and Smart Energy Grid. The assessment is conducted using a fused urban metabolism (UM)-life cycle assessment (LCA) approach, referred to as UM-LCA. The UM-LCA uses metabolic flows across an urban system as inputs and outputs in an LCA. All life cycle stages of the metabolic flows can be accounted for by using this approach and burden shifting from one stage to another is made quantifiable and hence transparent. The impact assessment is conducted using the ReCiPe method. The results obtained for the midpoint indicator, global warming potential (GWP), show reduced environmental performance effect at 75% relative to a business as usual reference scenario by introducing Smart Windows. Furthermore, the GWP indicator shows an environmental improvement of 10% for a Smart Energy Grid solution. Introduction of Pneumatic Waste Collection or Greywater Recycling reveals a minor negative performance effect of 0.76 and 0.70%, respectively, for GWP. The performance changes in terms of GWP for the remaining solutions are so small that these are expected to be within the uncertainty of the calculations. To obtain endpoint indicators (damages), the entire palette of ReCiPe indicators is included. The results of the endpoint indicator assessment yield a tendency similar to the one observed for climate change. It is found that the implementation of Smart City Solutions generally has a negative influence on the environmental sustainability performance of an urban system. The limited positive influence from the Smart City Solutions is due to burden shifting from the direct impacts of the urban system to embedded impacts which are out of sight for most policy makers. The influence of the Solutions on Copenhagen is generally small, due to a focus on reducing in areas that are not a large environmental burden in Copenhagen. The results are not sufficient to discard the idea of using Smart City Solutions to reduce environmental impacts, but highlight the importance of choosing solutions with the right focus and optimizing the design to best fit the intensions.

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.

Decentralized Cloud-SDN Architecture in Smart Grid: A Dynamic Pricing Model

Smart grids (SG) energy management system and electric vehicle (EV) have gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real-time price and demand–supply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on software define networking (SDN) technology and network function virtualization (NFV). We aim to schedule user's requests in a real-time way and to supervise communications between microgrids controllers, SG and user entities (i.e., EVs, electric vehicles public supply stations, advance metering infrastructure, smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all microgrids. We solve the problems by using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized Cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximizes EVs utility, and maintains the microgrid stability. The simulation is based on real electric load of the city of Toronto.

Smarter City: Smart Energy Grid based on Blockchain Technology

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger.

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids.

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

The promise of smart grids

ABSTRACTIt is the promise of smart grids – their anticipated role in meeting economic, social, environmental policy objectives – that is driving action on smart grids worldwide, while the reality is rather more messy. This paper is about the implementation of smart grids in Australia, and examines the degree to which environmental and social promises have materialised (or not) within two large energy smart grid initiatives undertaken in the period 2009–2014: the federal government-sponsored Smart Grid Smart City Program and the State of Victoria’s Advanced Metering Infrastructure Program. The analysis draws on a governmentality approach to examine how the promise of smart grids has not for the most part been delivered, concentrating in particular on how new digital technologies have not “behaved” in the way originally planned. Within a governmentality framework, it is generally assumed that technologies work to support government programmes, to accomplish governance. But growing evidence points to smart grid technologies undermining the promise of smart grids. Such a finding stands at odds with the assumption in governmentality about technologies doing work in consort with rationalities of government.