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P2P, CSC and TE: A Survey on Hardware, Software and Data

International Energy Research Centre (IERC), Tyndall National Institute, T12R5CP Cork, Ireland
Department of Electrical and Electronic Engineering, University College Cork, T12K8AF Cork, Ireland
Department of Computer Science, University of Bristol, Bristol BS8 1TH, UK
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2021, 14(13), 3851;
Submission received: 30 April 2021 / Revised: 18 June 2021 / Accepted: 22 June 2021 / Published: 26 June 2021


Peer-to-Peer (P2P), Transactive Energy (TE) and Community Self-Consumption (CSC) are exciting energy generation and use models, offering several opportunities for prosumers, micro-grids and services to the grid; however, they require numerous components to function efficiently. Various hardware devices are required to transmit data and control the generation and consumption equipment, whereas software is needed to use the gathered information to monitor and manage the hardware and energy trading. Data can be gathered from a variety of origins from within the grid and external sources; however, these data must be well-structured and consistent to be useful. This paper sets out to gather information regarding the hardware, software and data from the several archetypes available, focusing on existing projects and trials in these areas to see what the most-common hardware, software and data components are. The result presents a concise overview of the hardware, software and data-related topics and structures within the P2P, TE and CSC energy generation and use models.

1. Introduction

The global population is expected to grow significantly over the next 30 years, and is expected to hit almost 11 billion by 2050 [1], coupled with the electrification of heating, cooling and transport, this will have a profound effect on electricity consumption and demand [2]. The development of potential solutions to address this problem is of great importance. Peer-to-Peer (P2P), Community Self-Consumption (CSC) and Transactive Energy (TE) energy business and sharing models offer an innovative and exciting opportunity, but extensive obstacles and challenges as well. Discussions around implementation of such opportunities is vital if we are to tackle the Sustainable Development Goals [3] in relation to energy poverty, as energy is seen to be vital for improving living conditions and household capability [4].
However, the underlying challenges to the widespread adoption of these models are numerous [5]. As new renewable (and distributed) energy resources become available, the grid matrix becomes more complex to manage and integrate. The standardisation efforts, or the inadequacies they possess, poses serious difficulties as it can have high granularity with a diverse range of hardware and software implementations. Additionally, energy storage in distributed generation is very important as it needs to be flexible enough to support decentralised energy schemes, as it must be the basis to tackle unbalanced supply and demand scenarios.
To accomplish that, decentralised energy schemes, such as P2P, rely on a whole set of dedicated hardware and software solutions; therefore, such granular implementation and its integration to the power grid will demand state-of-the-art management systems [6]. Very innovative solutions [7] to support data integration, enhanced management features and trading functionalities [8] should be in place—understanding this environment is imperative.
However, researchers and practitioners are challenged with the task of remaining well-informed of the relevant developments on the topics of decentralised energy generation and use, given the:
  • current rapid developments in the research and practice on this topic, driven by the world-wide energy decarbonisation agenda, and
  • the wide set of relevant disciplines and knowledge areas—ranging from the power systems aspects of hardware, to networking and telecommunications; from software platforms for energy transactions to regulations on data protection and privacy.
This paper provides a concise overview of the current state of practice on hardware, software and data research topics for the P2P, CSC and TE energy business and sharing models. To the novice entrants into this vibrant field of research and development, this paper aims to serve as a guide that signposts the key directions of the ongoing work and to outline the breadth and the depth of the relevant issues. To those already working in this domain, the paper aims to provide a consolidated update on the current state of the wider field.
To provide this concise overview, the paper presents a structured literature review on the topics of hardware, software and data for the three previously noted decentralised energy models.
The paper is organised as follows: Section 2 briefly describes the methodology used to choose all the material supporting this research. Section 3 brings a comprehensive compilation of the main hardware devices used on P2P, CSC and TE implementations. Section 4 then describes a comprehensive compilation of software (including blockchain) commonly used in P2P, CSC and TE. Similarly, Section 5 addresses some software features but with a focus on interfaces and applications. Section 6 explores the data aspect of P2P, especially its diversity and integration issues. Section 7 explores the P2P, CSC and TE main concepts, with reference to its advantages and disadvantages. Section 8 studies a selected variety of P2P, CSC and TE projects and, finally, Section 9 discusses the main topics of this research. Additional supporting materials (acronyms and abbreviations, and an appendix with an extensive list of projects that were reviewed during this research) are attached.

2. Methodology

This paper employed a structured approach to the literature review, aiming to answer the following questions:
(1) What hardware and software were mentioned by P2P, CSC and TE projects (and were they trialled)?
(2) What data were gathered, what were they used for and who had access to them?
(3) What areas existed in the available documentation (publications, reports, white papers and websites) that requires further research?
Initially, the research focused on gathering publications, reports and associated information related to P2P, CSC and TE projects and trials using relevant keywords. Then, the structure was divided up into the following four categories, hardware, software, data and projects. Each paper gathered was then evaluated looking for the above terms and associated words (such as “Inverter”, “Network”, “Tool”, “Pilot”, “Demo”, “Trial”) with a matrix developed to determine if the source contained these words. This matrix helped inform the reviewers if the projects warranted further investigation (for example, projects with numerous mentions in publications, ticking the relevant categories, project websites, reports and publications). In short, the goal was to help find projects that could answer the above questions and had a correspondent that could be contacted in further stages of GO-P2P, to elicit more details that may have been lacking in publications or other such documentation.

3. Hardware

3.1. Hardware Requirements

Transactive energy systems, including that of a P2P energy trading system, requires end-use devices utilised with the smart grid environment. These control devices may be categorized as [9,10]:
  • Passive.
  • Active.
  • Interactive.
  • Transactive controllers.
  • Communications.
  • Controllers and actuators.

3.1.1. Passive

In short, a passive device is an uncontrolled element with no input regardless of its system’s frequency, congestion, peak prices and so on.

3.1.2. Active

Active devices are intelligent devices that respond autonomously to the reduction of consumption in accordance with the principle of demand response. The autonomous response does not enable users to have a direct input interface that would allow control of items, such as consumption reduction process.

3.1.3. Interactive

Interactive devices allow for limited interaction. Consumers can interact to adjust the consumption of electricity, but still cannot regulate it as per peak prices for example.

3.1.4. Transactive Controllers

These controllers are envisaged to be the most advanced form of devices that can exchange information between consumers, grid, aggregators and so on. This information can be about electricity prices, line congestion and frequency, resulting in the system as a whole to determine a more efficient way to operate and provide a win-win situation, to every consumer participating in it. These particular devices are the most favourable with regards to community P2P energy trading markets, as they possess characteristics that are optimal for a P2P framework, and allow both prosumers and active consumers to make trades in real-time. The transactive control device is primarily an energy gateway that exchanges information for energy transactions [11]. Transactive energy markets (P2P and local energy markets) require such controllers to communicate with the various hardware types such as smart meters, sensors, prosumer energy metering and EMS hardware. In context, the cooperative P2P energy trading market with two hardware devices; Smart Meters and Home Energy Management System (HEMS), at the prosumer or active consumer premises is vital in disrupting the energy market.
Smart meters are intelligent metering infrastructure, which is believed to, by replacing the traditional meters , offer a transition to a low-carbon energy system, by enabling better energy awareness amongst the participating consumers. It may also present sufficient information such as providing users access to accurate information on energy usage throughout the day, variable electricity pricing and also exhibit demand response capacities obligatory for a prosumer to participate in the bidding/auction process. Smart meters could be defined as an enabling piece of hardware that will incentivise the prosumers through the P2P and other markets. Thus, it is important to mention that the smart meters should be capable of participating in the blockchain platform by having sufficient computational power to track and trace the number and complexity of smart contract transactions. On the other hand, the HEMS is a hardware environment installed in the consumers’ home and acts as behind-the-meter technology envisaged to converge three large consumer segments, smart heating, home entertainment and home security systems. These can be used to control the on-site devices and aid the consumer in contributing to demand response.

3.2. Communications

Controllers and Actuators

To support the HEMS in controlling a variety of household devices, actuators are needed. Controllers can control and monitor electrical appliances while actuators constitute a final element to take actual physical action. The controller with certain optimization algorithm’s determines when to command actuators to turn on or off power to the particular appliances. This command is known as an information trigger. In the context of the demand response, the pre-designated trigger is responsible for actuating the response received by an actuator. Notification, pricing and control signals, local demand and frequency are a few triggers also included [12]. Based on type of technique used for scheduling loads, the HEMS controllers can be categorized into three categories [13]:
  • Rule-based controller.
  • Artificial intelligence-based controller.
  • Optimization methodology-based controller.
Rule-based controllers are the most straightforward way to realise a control operation in home automation. The control action is carried out based on some condition being met or violated possibly by developing some if/then ruling set. These ruling sets may include load shifting based on low prices, load priority, decrease in energy cost, etc. As this is the most simple control approach, it possesses certain shortcomings that cannot be extended for demand response and inevitably blockchain application due to the very limited data handling capabilities [14,15]. Recently, AI-based controllers’ found their application in the HEMS controller, as it can overcome the limitations of the rule-based controllers. A controller that mimics the human decision-making and thinking process would be ideal for scheduling control in smart homes. These controllers provide quick responses on fast solution-based predictions [16,17,18].
The only drawbacks of such controllers is their long learning and training times, which effects the solution and controller speeds. Moreover, there is no provision of addressing user preferences. To counter the shortcomings of the AI-based controllers, optimization-based controllers seem to be a perfect fit for scheduling intelligent control. These controllers are based on optimization methods capable of providing a suitable solution based on user preferences. These user preferences are realised as constraints subjected to determining (minimizing or maximizing error, cost, design and energy management) an objective function. A few applications include minimization of the electricity cost, optimal consumption schedule, minimize tariff for end users, controller scheduling with storage, optimal day-ahead scheduling and so on [19,20,21,22,23].
Control relays/actuators are the final elements to realise load control. These actuators work as load controllers, switching connected appliances on and off to minimise base-load energy consumption. This is performed via smart plugs, and power to the plug socket is cut when the system senses that the power consumption of the connected appliance corresponds to standby mode, or when external environmental conditions are met. The HEMS may include a number of sensors depending on the diversity and number of appliances that should be controlled. These sensors are connected through wireless communication to a common actuator. This network of sensors along with actuators is known as Wireless Sensor and Actuator Network (WSAN), which provides capability for different nodes to communicate with each other and carry out an actuation action. The plugged-in electrical loads such as a television, fan, washing machine, vacuum cleaner, lighting, etc., may be switched on or off by receiving a command from the actuator. Actuators send control commands to modify the switching status of a particular load through power socket sensor. For this, the actuators usually require consumption data and status of the loads. If there are a large number of sensors in a house, separate sensor sets will be controlled by varying other actuators [24,25,26]. The actuators may be a flush-mounted device, plugs or an in-built device. For example, a switching actuator, dimming actuator, multi-heating actuator and thermostat actuator [27]. It should be noted that these actuators are low-level computational hardware that cannot send information processing to and from a blockchain due to their limited computational capacities. Further research is required so that even these hardware may communicate with the blockchain platform with some additional low-cost computational element embedded in the hardware.

3.3. Hardware Data (Smart Meters, Actuators, Sensors, etc.)

Typically, mechanical meters collect data on an hourly or monthly basis, whereas more modern smart meters collect data by the minute. Grid operators have access to large volumes of data, from the massive number of devices connected to the power grid such as generators, breakers or even information about individual appliances in commercial or residential buildings. These massive amounts of data come from multiple sensor networks across transmission and distribution systems [28]. Data are gathered from various sources, such as electricity costs, billing information, Phasor Measure Unit (PMU)s, smart meter measurements, weather conditions and SCADA. It is critical that the data are made available so that useful information can be obtained [28].

3.4. Monthly Energy Collection by Smart Meters

Energy meters gather monthly information about household electricity consumption. As the meter may not read the energy usage at a set time interval, the data provided by the meter may be limited. A smart meter provides information on Voltage (V), Real Power (watt), and Apparent Power (VA) consumption at a 15-minute sampling rate. It is possible to collect monthly data and report it from a given start and end date. [28] It is recommended that, if billing is the sole purpose, reporting monthly data will reduce communication needs between the meter and control centre [28].

3.5. Daily Energy Collection by Smart Meters

Energy consumption may reveal minimum, average and maximum energy consumption on a daily basis in a household [28]. This information can be used to determine occupancy, the number of individuals in a household and activity in the household. Since this information is strictly private, consumers may raise concerns (i.e., General Data Protection Regulation (GDPR)). Daily consumption information varies due to weather conditions, individuals’ behaviour and preference, occupancy and therefore is of little use unless there is abnormality in energy consumption. However, in HEMS, daily energy consumption information can be vital to the monitoring and triggering of customer actions in case of abnormality [28].

3.6. Smart Sensors in a Smart Grid

Smart sensors are capable of recording very valuable information. These are intelligent devices deployed in the smart grid, some of which include temperature, pressure, humidity, voltage and current sensors. The authors of [29] describe the sensing functions, applications and network communication of PMU and Merging Unit (MU)-based smart sensors. A PMU provides real-time synchrophasor data for advanced applications [29]. It is a device that produces synchronised phasor, frequency and Rate of Change of Frequency (ROCOF) estimates using the input voltage and/or current signals [29].
  • Current and voltage sensors provide analogue inputs for PMU, used to measure current waveforms and 50/60 Hz alternating current (AC) voltage, typically sampled at a rate of 48 samples per cycle [29].
  • An internal clock records the timestamp for each sample and accurately synchronizes the time using a phase-locked oscillator [29].
  • A processing module follows various steps. Signal conditioning is performed, followed by the conversion of signals into digital form with timestamps using a phase-locked oscillator and alignment of phase data using a GPS time reference of an external network time reference. Finally, the voltage and current phasors are computed, and synchronised phasor, frequency and ROCOF estimates are produced [29].
  • The network communication module is used to communicate data with advanced applications.
A MU-based smart sensor provides the real-time status of the power grid [29]. It is a device that samples Alternating Current (AC) signals in one or more phases. It converts the voltage and current signals into digital values, uses time synchronisation to align multiple phases together and transmits the sampled values to protection replays [29]. The main functions of MU-based smart sensors includes sensing, signal conditioning, conversion of analogue signals to digital values, data processing and merging and network communication.
  • Analogue current and voltage of the power grid are provided as inputs to MUs.
  • The internal clocks record the timestamps for each sample and accurately synchronizes time with an external Coordinated Universal Time (UTC) source as a time reference [29].
  • In the processing module, amplification and signal conditioning are performed, the analogue signals are converted into digital form based on data from GPS receiver or external network time reference. An alignment algorithm is used to produce three time-synchronised phases of voltages and currents [29].
  • The network communication is used to communicate with intelligent electronic devices and applications; the authors of [29] discuss IEEE Standards for PMU and MU-Based Smart Sensors, which describe the standard that these devices follow, such as the Institute for Electrical and Electronic Engineers (IEEE) C37.118 Standard for PMU-Based Smart Sensors and the ieee 1815 Standard for Smart Sensors.

4. Software, Including Blockchain

Data are a major factor to be considered; GDPR 2016/679 in the European Union (EU) have put in place strict rules for the processing of data.

4.1. Blockchain

Blockchain is a transparent, reliable and network-friendly approach that fulfils the requirements of security, privacy and payment transactions in distributed energy trading systems [30]. Blockchain is an emerging technology allowing public contracts and secure transactions over a peer-to-peer distributed network while ensuring security and privacy of user information [30]. This secure system prevents information forgery as the data in the chain cannot be altered, while newly entered data must be verified [31]. The blockchain technology enables the storage, exchange and verification of data in a distributed manner, as well as the traceability of activities and the anonymity of users. Users are identified by cryptographic public keys, which enables a high level of anonymity [30]; however, this is pseudonymisation and not anonymisation. This means that a user’s activities, such as energy consumption patterns, timestamps, location, etc., can be used to identify more sensitive information. The authors of [32,33] discuss the zero knowledge proof techniques used to address this anonymity-based weakness. Blockchain can be perceived as a trustless trust, which allows the facilitation of transactions and shared history between its users, and that it is trustworthy without the use of trust between users [34]. Thus, it has high potential for allowing integration into the network, such as smart meters, efficiently balancing the grid by harmonising the supply and demand in real-time [31]. Additionally, it facilitates electronic contracts, called smart contracts, between distributed energy traders and consumers. These smart contacts enables two or more users to participate in a trade contract with each other anonymously. It is essentially a transaction protocol that executes the terms of the contact [35]. In blockchain, smart contracts are stored with a unique address and are triggered by addressing a transaction to it. Then, according to the data included in the transaction that triggered the execution, it is automatically and independently executed on every node in the network in the given manner [35]. The data associated with a smart contract include program logic, a unique contract identifier, owner of the contract, account balance and private data storage [30].

4.2. How Is It Beneficial to Energy Trading?

Blockchain can support the energy trading of Plug-In Electric Vehicle (PEV)s, which dynamically enter and leave the smart grid network at different locations [30]. These features make blockchain an essential technology for contribution to the distributed energy trading market.

4.3. Comments on Blockchain

As with many technologies, blockchain is not perfect. An increased use of a blockchain ledger will ultimately slow down its algorithmic speed, and increases its dependencies on resources. This was echoed in the city of Plattsburgh, New York, where the mayor banned the mining of Bitcoin due to concern over electricity usage. The imposed moratorium of 18 months was given after it was found that Bitcoin operations were accounting for 10% of the city’s usage [36].

5. Software Data (Interfaces and Applications)

Software and the User

The basis of any software model is the user interface, referred to as the Graphical User Interface (GUI), and the background processing of data, communication with the server or other computers on the network. The GUI needs to have a user-friendly approach that allows the user to interact with the system with ease. The principle purpose of any energy trading platform is the interaction between peers, i.e., principally energy suppliers to list the energy that they have available for sale, and the consumers to be able to purchase that energy. To achieve optimal experience for the users of the software platform, the use of databases should allow the system to filter and analyse user requirements, and ultimately the result should allow the system to provide effectiveness and certainty whilst reducing transaction costs and providing transparency [37].
Consumer and prosumer apprehension must certify that energy can be bought, sold and payment can be received. This must be achieved through the usability of the software, with a build of trust [34].

6. Data

A significant part of the discussion about energy efficiency relies on energy digitalisation in order to provide the necessary means for intelligent and efficient operation and management [38]. By properly extracting and exploring the data present in the energy sector—whatever the infrastructure level—it will be possible to boost energy efficiency to a whole new level.
The access to the data and their correct use (always compliant with GDPR or any other regulations and policies) is at the core of the P2P scheme no matter if it concerns automation, connectivity, regulation or the empowerment of consumers/prosumers and several other stakeholders.
Although systems (hardware and software) vary enormously throughout the diverse types of implementations [39] (in what concerns diverse technologies, regulations, standardisation efforts or the lack of it and so on), the concept of an efficient bi-directional communication [40] is mandatory to guarantee interoperability.
One of the most important issues for Peer-to-Peer is how exactly to support the integration of data in a way that they can support innovative systems [8] and all sorts of energy community sharing and trading [5,8]. Table 1, Table 2 and Table 3 show only a few examples of how diverse the energy data can be: Internet of Things (IoT) temperature readings, Photovoltaic (PV) readings and data for a district heating system substation operation (natural gas as primary fuel).
Standardisation of such diverse energy systems and technologies is in it initial stages—if at all; therefore, the process of integration of these data sources in a coherent and usable way must be addressed properly. To tackle this challenge, the development of an ontology-based standardisation initiative can be a important action to foment data integration and, on a higher level, contribute to new regulations and policies.

6.1. Ontology

The devices participating in a P2P implementation need to share a common structure of data communication to allow users (consumers and/or prosumers, regulators, etc.) and systems to share valuable information that will support operation, performance, trade, pricing and so on. A prosumer is an individual, association or even a company that both consumes and produces energy (especially when the generation surplus can be trade in a P2P scheme).
An ontology can be a powerful tool [39,41] to deal with these challenges. The work of [42] suggests an ontology framework (Figure 1) to analyse and develop a standardisation effort of distinct components found in a P2P system, defining key concepts to interconnect systems by suggesting a neutral language to integrate such components. An example of the application of this ontology can be seen in the use case detailed in the following subsection.

6.2. Use Case

The aforementioned ontology is part of the Cooperative ENergy Trading System (CENTS) project—a project coordinated by the International Energy Research Centre (IERC) and in partnership with industry experts, such as mSemicon [43], Community Power [44] and the research centres of University College Cork (UCC) [45], National University of Ireland Galway (NUIG) [46] and Technological University Dublin (TUD) [47].
CENTS is a platform for the electricity sector to connect users (consumers, producers and prosumers) and communities, which are empowered with the infrastructure to generate their own electricity, earn from the surplus electricity production and finally contribute to decarbonisation policies in their homes and communities.
The use of this ontology scheme backed the work of defining how the sensors and IoT devices—PV readings, energy storage (battery status), temperature and any other meters and sensors in general—could be integrated into the CENTS platform. By doing so, the ontology makes important contribution to the development of data “backbone” that will be the base for the energy trading and user empowerment.

7. P2P

Peer-to-peer systems in networking terms can be defined as a collection of links that connect the underlying nodes at the application layer and can be defined as a single-overlay or multi-overlay system, and these can be broken down into further subcategories [51].

P2P in Energy Trading

The first use of the peer-to-peer trade in the energy space occurred in 2016, when the New York microgrid allowed a resident to trade with a neighbour [52]. As the use of renewable energy grows and the distributed network develops, it is believed that peer-to-peer energy trading will also develop allowing for the monitoring of not only the power network, but the use consumption as well [30].
Advantages: presently, the main production of power is through centralised systems that can be subject to down time due to varying reasons. A decentralised model allows for consumers to have a greater reliability of source due to their local occurrence nearer to a prosumer [53]. In areas where electricity supply could be intermittent, or experience blackout to weather issues, the decentralised model could be a solution to aid the continual supply of energy. Projects such as SOLShare [54] have created a P2P system for off-the-grid areas in Bangladesh and India [55]. Bangladesh may be the global leader in solar installations, but for many families, without solar installations, reliance on neighbours is the only availability of energy [56]. The ability to purchase electricity from other community members allows for all houses to be electrified and eliminate energy poverty in these areas. P2P has also been seen as a solution to blackouts caused by catastrophic weather events in the USA.
Disadvantages: P2P can also cause an unfairness problem between participating users. It may also lead to less oversight of data being handled, which may impact data privacy and may cause a GDPR breach.

8. Examination of Existing Projects

The following is a subset of the entire list of projects that were reviewed during this research, the full list is contained within the appendix. The authors reviewed over 150 projects and trials and reduced this to the following for further, more detailed analysis.
The authors have extensive experience in P2P, CSC and TE, having been involved in several past and current energy projects funded nationally and the EU, as part of these projects, we would have carried out state-of-the-art analysis on several projects, including those in Table 4 and in Appendix A. The focus of this paper was to help identify the hardware, software and data ontologies used in these projects, which will help guide future research on recommended set-ups for the various archetypes that exist.

8.1. Selection of Established and New Worldwide Projects

Piclo [57]: Introduced in 2015 in the UK, Piclo was the first online P2P electricity trading platform. It is the UK’s leading software program for democratisation and decentralised electricity market [31]. It was a collaboration between “Open Utility”, an innovative technological company, and “Good Energy”, a renewable energy supplier, where consumers were enabled to purchase electricity directly from the producers of renewable energy [58].
In the peer-to-peer energy match trading system, smart meter data, producer/generator pricing and information about consumer preferences were used to match demand and supply of electricity energy half hour [31]. This method enabled registered consumers to choose the producers/generators that they wanted to purchase from. The matching of consumer and producer involves factors such as preferences, prioritised producers/generators, location, cost and ownership as well as various data visualisation and analytics [58]. Good Energy plays an important role in Piclo to maintain a balance in the marketplace by providing essential information such as contracts, billing and smart meter data [58].
Vandebron [66]: Developed in the Netherlands, Vandebron is an online energy platform that allows energy consumers and prosumers to purchase electricity directly from independent producers [58]. The platform brings together supply and demand for ethical energy sources and links the producers of sustainable energy to households. The energy marketplace provides consumers with a selection of energy producers such as solar, wind and bio-energy.
Elecbay [67]: This platform, designed by Cardiff University (UK), allows users to forecast their own energy generation vs. consumption, and creating schedules for the purchase and selling of electricity. It is believed that is it one of the first platforms for LV distribution networks energy trading, where trading occurs between consumers, prosumers and energy suppliers [68]. In the case of a seller unable to generate the promised scheduled trade value, a penalty will be set, and the seller will have to fulfil their contract by purchasing power from another seller.
Smile [63]: Smile is a project that has been demonstrated on three pilot islands, with various methods of renewable generation. PowerShare was the platform that was developed, which was a mobile app that connects to the Energy Trading Management system, and allows users to monitor trade and consumption [34].
Energy Web [69]: Launched in 2019, the Energy Web is a non-profit organisation focusing on building a core infrastructure with the use of open-source and decentralised technologies. A blockchain system supported by international companies, it has acquired 25 validators, over 17 countries, within its first year, and is working with these international companies to develop its tools and systems. It is solely developed as a platform provider, allowing for it to be a central point of an energy-sector community [70].

8.2. Project Shortcomings and Regulation

Yehola [71] began as a project in 2012 to educate and encourage solar usage between communities. As the project developed, it became a solar sharing network with a subscription web-based platform marketplace that allowed non-solar-panel users to also buy green energy, but due to lack of finance and regulatory instability, Yehola ceased trading in 2015.
Regulatory capping on renewable fuels around the world differ between countries and regions, and also provides barriers to the generation and use of renewable energies.
In the USA, net metering caps, which differ from state to state, can limit the solar energy producers returns on selling excess energy to the grid, tax breaks or the number of installations the state will allow [72]. Such inhibitions are stalling the growth of the renewable energy market, especially that of solar, which can be easily installed for many users; however, some states, such as South Carolina, see the renewable energy market as a way to deliver resilient power. For many years, South Carolina has relied on fossil fuel generators to provide power through blackouts caused by extreme weather events, but as these events are on the rise with climate change, elected officials are looking toward other methods of providing backup power [73]. This resulted in the publication of the “Energy Freedom Act”, which requires utilities to explore solar plus storage generation assets and lifted the 2% net metering cap [74].
Germany, a country that is seen to be a forerunner in renewable energy generation, removed solar generation caps in 2020, and minimum distancing with regard to onshore wind turbines, after the country saw a drop in 2019 in wind energy generation, mainly due to regulatory issues [75]. It is hoped that this change, i.e., the shift of the political strategy, will assist Germany in meeting its 2030 30% renewable target.
In Japan, consumers can only purchase electricity from a retail power company, so to enable the development of renewable energy and blockchain, differing retail power companies have partnered with different projects to trial platforms. PowerLedger have partnered with electricity retailer eRex [76], along with other partnerships with electricity retail partners including L03, Ricoh and the recent collaboration of Mitsubishi Electric and Tokyo Institute of Technology to develop a system that can commercialise at the earliest possible opportunity [77].

9. Conclusions and Discussion

As mentioned, Germany is a forerunner in renewable energy adoption in Europe, as such, it would not be surprising to say that Germany is also a forerunner in adopting blockchain. September 2019 saw a commitment to the use of blockchain across various industries. The adoption of the national blockchain plan has seen the development of regulatory measures crossing various aspects of the blockchain plan. In the energy space, a dedicated “Future Energy Lab” was developed and launched in August 2020, as a project by the German Energy Agency, to serve as a networking centre and a point of contact [78].
Hardware is a critical part of any P2P/TE/CSC project, enabling the transmission of data and the control of necessary equipment. The basic hardware elements of P2P projects are consistent across most of the P2P/TE/CSC projects analysed, although the setup and design parameters of the different hardware components vary significantly. The meters and sensors needed for the projects to function can be positioned and set up in a multitude of configurations and to provide varying amounts of data. Similarly, types of controllers and actuators in the system can vary between rule-based to Artificial Intelligence (AI)-based, which rely on ever increasing amounts of data.
Differences in sensor hardware precision and/or accuracy, while monitoring similar phenomena, must be addressed properly. The sensors can duplicate each other or just have partial overlap (competitive measurements), or be combined to create a more accurate observation of a phenomenon (cooperative measurements). Nevertheless, to achieve these results, the sensor outputs must be integrated, and eventually rectify and/or recombine the data if they disagree. Usually, the integration of sensor data is achieved by algorithms with different levels of complexity to compile coherent results. Additionally, the algorithm must be able to manage vast amount of data in order to condense the data into useful and manageable results.
Software for any P2P/TE/CSC project can be classified as critical, as it is the enabler for the system to allow the users/actors throughout the system to interact with it and allows for the analysis of metrics, forecasting as well as many other aspects. This software interface can take many constructs and be developed in many different ways, such as through a mobile or web application, or simply as an interaction-enabled interface on a smart meter or device. As with any system, the software is based on the system design, moulded with the user’s requirements and can be seen as only relative to that project or solution. Thus, even though we have discussed various projects in this review, the software developed for each project can be defined as only relevant to the project that it belongs to; therefore, little foresight is given into software solutions for the varying projects, with more emphasis given toward the physical network or project motivation.
Data are a valuable part of any system as they allow the establishment of baselines, benchmarks, facts and statistics. In a P2P/TE/CSC model, data are often gathered through various sources including hardware (such as smart meters, actuators and sensors) and via software tools. Large volumes of data are being collected continuously from these hardware and software systems. Data are widely available in many formats, however, these data are found to be largely unstructured, which is a challenge we face in decentralised models.
There are many opportunities that exist for standardisation in P2P/TE/CSC. By focusing on archetypes as the root of these models, branching out to identify recommended hardware, software and data ontologies for these archetypes will prove beneficial to future trials and implementation of the models.
As stated in the introduction, this paper has outlined the breadth and depth of the hardware, software and data-related issues of current research and practice in decentralised energy generation and use models, so as to help signpost and navigate the wide and varied aspects of this domain. In terms of further outlook, this work will continue to focus on helping the P2P, TE and CSC research and practitioners to, on the one hand, better appreciate the complexities and interactions across the hardware, software and data aspects discussed above, and, on the other hand, to practically replicate successful solutions for businesses. To this end, we plan to undertake a number of case studies of cutting edge P2P, TE and CSC research and practice pilots and projects. Each case study will present the specific hardware, software and data solutions, along with the relevant context for their successful operation and adoption as well as the related research and practice challenges. Through this work we aim to both foster further research on practice-driven challenges and facilitate example-based practical adoption of these energy generation and use models.

Author Contributions

Conceptualization, B.O. and F.S.; methodology, B.O.; validation, B.O., B.H., V.H., R.C. and P.L.; investigation, B.O., F.S., E.O., F.T., K.M. and R.C.; resources, B.O. and F.S.; writing—original draft preparation, B.O., F.S., E.O., F.T. and K.M.; writing—review and editing, B.O., F.S., E.O., F.T., K.M., B.H., V.H., R.C. and P.L.; visualization, F.S.; supervision, B.O. and F.S.; project administration, B.O. and F.S.; funding acquisition, B.O. All authors have read and agreed to the published version of the manuscript.


This research was generously funded by the Irish Government, specifically the Department of the Environment, Climate and Communications.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.


This publication acknowledges the support and the work of the Global Observatory on Peer-to-Peer (P2P), Community Self-Consumption (CSC) and Transactive Energy (TE) Models (GO-P2P).

Conflicts of Interest

The authors of this paper are currently involved in the Cooperative Energy Trading System (CENTS) project and have NO agreements with study sponsors, both for-profit and non-profit, that interfere with authors’ access to all of the study’s data or that interfere with their ability to analyse and interpret the data and to prepare and publish manuscripts independently when and where they choose. Proper acknowledgement of support and funding is clearly stated in Section 9 (Acknowledgements) and the authors also declare NO conflict of interests that could inappropriately influence or bias the work presented in this paper.


The following abbreviations are used in this manuscript:
ACAlternating Current
AIArtificial Intelligence
CENTSCooperative ENergy Trading System
CSCCommunity Self-Consumption
EUEuropean Union
GDPRGeneral Data Protection Regulation
GUIvGraphical User Interface
HEMSHome Energy Management System
IEEEInstitute for Electrical and Electronic Engineers
IERCInternational Energy Research Centre
IoTInternet of Things
MUMerging Unit
NUIGNational University of Ireland Galway
PEVPlug-In Electric Vehicle
PMUPhasor Measure Unit
ROCOFRate of Change of Frequency
TETransactive Energy
TUDTechnological University Dublin
UCCUniversity College Cork
UTCCoordinated Universal Time
WSANWireless Sensor and Actuator Network

Appendix A

Table A1. P2P, TE and CSC projects and trials.
Table A1. P2P, TE and CSC projects and trials.
Project NameWebsite
4New Cryptocurrencies (accessed on 30 April 2021)
Alliander (Alva) (accessed on 30 April 2021) (accessed on 30 April 2021)
Alliander & Spectral Energy (Jouliette at De Ceuvel) (accessed on 30 April 2021)
ampcontrol-Alliander (Charge Ledger) (accessed on 30 April 2021)
Assetron Energy Cryptocurrencies (accessed on 30 April 2021) (accessed on 30 April 2021)
Bankymoon Metering
BAS Nederland Metering
BCDC (Blockchain Development Company) Cryptocurrencies
BCPG Group Decentralised
Bettergy (accessed on 30 April 2021)
BEYOUND (accessed on 30 April 2021)
Bittwatt Decentralised
BLOC (EnergyBlock & Community Power)
Blockchain Energy (accessed on 30 April 2021)
Blockchain Futures Lab
Blockchain Research Lab
Bouygues Immobilier & Stratumn
Brookyln Microgrid
Car eWallet
CENTS (accessed on 30 April 2021)
CGI & Eneco
Community First! Village (accessed on 30 April 2021)
Department of Energy, US IoT, smart devices, automation & asset management
Department of Energy, US Metering, billing & security
Drift (accessed on 30 April 2021)
ElectriCChain (SolarCoin)
Electrify.Asia (accessed on 30 April 2021)
Electron (accessed on 30 April 2021)
Electron Grid management
Electron Metering
Elegant Metering
Empower (accessed on 30 April 2021)
Endesa Energia
Enerchain (accessed on 30 April 2021) (accessed on 30 April 2021)
Energi Mine
Energo Labs
Energo Labs
Energy Bazaar
Energy Collective
Energy Web Foundation
Energy-Blockchain Lab & IBM
Energy21 & Stedin
Enervalis (NRGCoin)
EU Blockchain Observatory
Eurelectric (Blockchain Discussion Platform)
Evolve Power
Filament Grid management
Filament IoT, smart devices
Freeelio (AdptEVE)
Green Energy Wallet
Green Running (Verv)
Grid Singularity
Grid+ (accessed on 30 April 2021)
Gridwiz (accessed on 30 April 2021)
Hive Power
IBM & Linux Foundation (Hyperledger)
Innogy Motionwerk (Share & Charge)
Intrinsic ID & Guardtime
Kansai Electric (accessed on 30 April 2021) (accessed on 30 April 2021)
KEPCO (accessed on 30 April 2021)
Level10 (accessed on 30 April 2021)
Lichtblick Swarm Energy (accessed on 30 April 2021)
Lition (accessed on 30 April 2021)
LO3Energy (accessed on 30 April 2021)
Marubeni (Coincheck Denki)
ME SolShare (accessed on 30 April 2021)
Nasdaq New York Linq
NOBEL (accessed on 30 April 2021)
NRGcoin (accessed on 30 April 2021)
OurPower (CEDISON)
Oursolargrid & ITP
Oxygen Initiative
P2P-SmartTest (accessed on 30 April 2021)
P2P3M (accessed on 30 April 2021)
Peebles/Siemens (accessed on 30 April 2021) (accessed on 30 April 2021)
PeerEnergyCloud (accessed on 30 April 2021)
Piclo (accessed on 30 April 2021)
Platinum Energy Recovery
PONTON (EnerChain)
Power Ledger
Pylon Network
Restart Energy
Smart Watts (accessed on 30 April 2021)
Solar bankers
SolarChange (SolarCoin)
SonnenCommunity (accessed on 30 April 2021)
SP Energy Networks, SSEN, SP Distribution, SP Manweb & UK Power Networks
Spectral Energy
State Grid Corp of China (accessed on 30 April 2021)
Street2Grid (accessed on 30 April 2021)
SwytchX (accessed on 30 April 2021)
Tavrida Electric
Tennet & Sonnen
Tennet & Vandenbron
The Sun Exchange
TransActive Grid
VAKT & partners (including BP, Shell & Statoil)
Vandebron (accessed on 30 April 2021)
Vattenfall (Powerpeers)
Vector Energy (EcoChain)
Veridium Labs
Volts Markets
Wien Energie
Wuppertal Stadtwerke (Tal. Markt)
Yeloha Mosaic (accessed on 30 April 2021)


  1. United Nations; Department of Economic and Social Affairs; Population Division. World Population Prospects Highlights, 2019 Revision Highlights, 2019 Revision; United Nations: New York, NY, USA, 2019. [Google Scholar]
  2. OECD (Ed.) Energy; OECD Green Growth Studies; OECD: Paris, France, 2012. [Google Scholar]
  3. GoI. Climate Action Plan 2019 to Tackle Climate Breakdown; Government of Ireland: Dublin, Ireland, 2019.
  4. Accelerating Sdg 7 Achievement Policy Briefs in Support of the First Sdg 7 Review at the un High-Level Political Forum 2018; United Nations Development Programme (UNDP) and University of Bergen: New York, USA, 2018.
  5. Cuenca, J.; Jamil, E.; Hayes, B. Energy Communities and Sharing Economy Concepts in the Electricity Sector: A Survey. In Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I CPS Europe), Madrid, Spain, 9–12 June 2020; pp. 1–6. [Google Scholar] [CrossRef]
  6. Silva, F.; O’Regan, B. An Innovative Smart Grid Framework for Integration and Trading; ICSREE2021: Strasbourg, France, 2021. [Google Scholar]
  7. CENTS Project. Available online: (accessed on 30 April 2021).
  8. Hayes, B.; Thakur, S.; Breslin, J. Co-Simulation of Electricity Distribution Networks and Peer to Peer Energy Trading Platforms. Int. J. Electr. Power Energy Syst. 2020, 115, 105419. [Google Scholar] [CrossRef]
  9. Chassin, D.P. Multi-Scale Transactive Control In Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios. Ph.D. Thesis, University of Victoria, Victoria, BC, Canada, 2017. [Google Scholar]
  10. Siano, P.; De Marco, G.; Rolan, A.; Loia, V. A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets. IEEE Syst. J. 2019, 13, 3454–3466. [Google Scholar] [CrossRef]
  11. Hu, J.; Yang, G.; Kok, K.; Xue, Y.; Bindner, H.W. Transactive Control: A Framework for Operating Power Systems Characterized by High Penetration of Distributed Energy Resources. J. Mod. Power Syst. Clean Energy 2017, 5, 451–464. [Google Scholar] [CrossRef] [Green Version]
  12. Chuang, A.; Gellings, C. Demand-Side Integration in a Restructured Electric Power Industry; CIGRE: Paris, France, 2008. [Google Scholar]
  13. Shareef, H.; Ahmed, M.S.; Mohamed, A.; Al Hassan, E. Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access 2018, 6, 24498–24509. [Google Scholar] [CrossRef]
  14. Althaher, S.Z.; Mutale, J. Management and Control of Residential Energy through Implementation of Real Time Pricing and Demand Response. In Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22–26 July 2012. [Google Scholar]
  15. Ahmed, M.S.; Shareef, H.; Mohamad, A.; Abd Ali, J. Rule Base Home Energy Management System Considering Residential Demand Response Application. Appl. Mech. Mater. 2015, 785, 526–531. [Google Scholar] [CrossRef]
  16. Ahmed, M.S.; Mohamed, A.; Homod, R.Z.; Shareef, H. Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy. Energies 2016, 9, 716. [Google Scholar] [CrossRef] [Green Version]
  17. Gharghan, S.K.; Nordin, R.; Ismail, M.; Abd Ali, J. Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling. IEEE Sens. J. 2016, 16, 529–541. [Google Scholar] [CrossRef]
  18. Yuce, B.; Rezgui, Y.; Monjur, M. ANN–GA Smart Appliance Scheduling for Optimised Energy Management in the Domestic Sector. Energy Build. 2016, 111, 311–325. [Google Scholar] [CrossRef]
  19. Setlhaolo, D.; Xia, X.; Zhang, J. Optimal Scheduling of Household Appliances for Demand Response. Electr. Power Syst. Res. 2014, 116, 24–28. [Google Scholar] [CrossRef]
  20. Mohsenian-Rad, A.; Leon-Garcia, A. Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Trans. Smart Grid 2010, 1, 120–133. [Google Scholar] [CrossRef]
  21. Guo, Y.; Pan, M.; Fang, Y. Optimal Power Management of Residential Customers in the Smart Grid. IEEE Trans. Parallel Distrib. Syst. 2012, 23, 1593–1606. [Google Scholar] [CrossRef]
  22. Tsui, K.M.; Chan, S.C. Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing. IEEE Trans. Smart Grid 2012, 3, 1812–1821. [Google Scholar] [CrossRef]
  23. Ahmed, M.S.; Mohamed, A.; Khatib, T.; Shareef, H.; Homod, R.Z.; Ali, J.A. Real Time Optimal Schedule Controller for Home Energy Management System Using New Binary Backtracking Search Algorithm. Energy Build. 2017, 138, 215–227. [Google Scholar] [CrossRef]
  24. Veleva, S.; Davcev, D.; Kacarska, M. Wireless Smart Platform for Home Energy Management System. In Proceedings of the 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, Manchester, UK, 5–7 December 2011; pp. 1–8. [Google Scholar] [CrossRef]
  25. Soetedjo, A.; Nakhoda, Y.; Saleh, C. An Embedded Platform for Testbed Implementation of Multi-Agent System in Building Energy Management System. Energies 2019, 12, 3655. [Google Scholar] [CrossRef] [Green Version]
  26. Ford, R.; Stephenson, J.; Brown, N.; Stiehler, W. Energy Transitions: Home Energy Management Systems; Centre for Sustainability at the University of Otago: Otago, New Zealand, 2014. [Google Scholar]
  27. Eaton. xComfort-RF Smart Home Solutions; Eaton: Dublin, Ireland, 2019. [Google Scholar]
  28. Lu, N.; Du, P.; Guo, X.; Greitzer, F.L. Smart Meter Data Analysis. In PES T&D 2012; IEEE: Orlando, FL, USA, 2012; pp. 1–6. [Google Scholar] [CrossRef]
  29. Song, E.Y.; FitzPatrick, G.J.; Lee, K.B. Smart Sensors and Standard-Based Interoperability in Smart Grids. IEEE Sens. J. 2017, 17, 7723–7730. [Google Scholar] [CrossRef] [PubMed]
  30. Abdella, J.; Shuaib, K. Peer to Peer Distributed Energy Trading in Smart Grids: A Survey. Energies 2018, 11, 1560. [Google Scholar] [CrossRef] [Green Version]
  31. Mujeeb, A.; Hong, X.; Wang, P. Analysis of Peer-to-Peer (P2P) Electricity Market and Piclo’s Local Matching Trading Platform in UK. In Proceedings of the 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China, 8–10 November 2019. [Google Scholar]
  32. Sasson, E.B.; Chiesa, A.; Garman, C.; Green, M.; Miers, I.; Tromer, E.; Virza, M. Zerocash: Decentralized Anonymous Payments from Bitcoin. In Proceedings of the 2014 IEEE Symposium on Security and Privacy, San Jose, CA, USA, 18–21 May 2014; pp. 459–474. [Google Scholar] [CrossRef] [Green Version]
  33. Miers, I.; Garman, C.; Green, M.; Rubin, A.D. Zerocoin: Anonymous Distributed E-Cash from Bitcoin. In Proceedings of the 2013 IEEE Symposium on Security and Privacy, San Francisco, CA, USA, 19–22 May 2013; pp. 397–411. [Google Scholar] [CrossRef] [Green Version]
  34. Lamas, D.; Loizides, F.; Nacke, L.; Petrie, H.; Winckler, M.; Zaphiris, P. (Eds.) Human-Computer Interaction—INTERACT 2019. In Proceedings of the 17th IFIP TC 13 International Conference, Paphos, Cyprus, 2–6 September 2019, Part III; Lecture Notes in Computer Science, Springer International Publishing: Cham, Switzerland, 2019; Volume 11748. [Google Scholar] [CrossRef]
  35. Tushar, W. A Coalition Formation Game Framework for Peer-to-Peer Energy Trading. Appl. Energy 2020, 261, 114436. [Google Scholar] [CrossRef] [Green Version]
  36. Villas-Boas, A. For the First Time, a US City Has Banned Cryptocurrency Mining after Large-Scale Operations Used up All Its Power. 2018. Available online: (accessed on 30 April 2021).
  37. Pouttu, A.; Haapola, J.; Ahokangas, P.; Xu, Y.; Kopsakangas-Savolainen, M.; Porras, E.; Matamoros, J.; Kalalas, C.; Alonso-Zarate, J.; Gallego, F.D.; et al. P2P Model for Distributed Energy Trading, Grid Control and ICT for Local Smart Grids. In Proceedings of the 2017 European Conference on Networks and Communications (EuCNC), Oulu, Finland, 12–15 June 2017; pp. 1–6. [Google Scholar] [CrossRef]
  38. Verma, D.P.; Strüker, D.J.; Kjeldsen, M.O.; Wang, X. Digitalization: Enabling the New Phase of Energy Efficiency; Group of Experts on Energy Efficiency GEEE-7/2020/INF.3; UNECE, United Nations, Economic and Social Council: Geneva, Switzerland, 2020. [Google Scholar]
  39. Yee Chong, A.T.; Mahmoud, M.A.; Lim, F.C.; Kasim, H. Description for Smart Grid: Towards the Ontological Approach. In Proceedings of the 2020 8th International Conference on Information Technology and Multimedia (ICIMU), Selangor, Malaysia, 24–26 August 2020; pp. 218–222. [Google Scholar] [CrossRef]
  40. Ouahada, K.; Longe, O. (Eds.) Smart Energy Management for Smart Grids; MDPI AG: Basel, Switzerland, 2020. [Google Scholar]
  41. Dori, D.; Sillitto, H. What Is a System? An Ontological Framework; Wiley Online Library: Hoboken, NJ, USA, 2017; pp. 207–219. [Google Scholar]
  42. Silva, F.; O’Leidhin, E.; Tahir, F.; Mould, K.; O’Regan, B. System Integration and Data Models to Support Smart Grids Energy Trading; ECRES: Kayseri, Turkey, 2021. [Google Scholar]
  43. mSemicon|Custom Product Development. Available online: (accessed on 30 April 2021).
  44. Community Power. Available online: (accessed on 30 April 2021).
  45. World-Class Undergraduate and Postgraduate Education in Ireland. Available online: (accessed on 30 April 2021).
  46. NUI Galway-NUI Galway. Available online: (accessed on 30 April 2021).
  47. TU Dublin-City Campus|Technological University Dublin. Available online: (accessed on 30 April 2021).
  48. Anand, A. Temperature Readings: IOT Devices (Relational Dataset from IOT Devices to Record Temperaure Readings). 2021. Available online: (accessed on 30 April 2021).
  49. Sharma, A. Solar Panel PV System Dataset. 2021. Available online: (accessed on 30 April 2021).
  50. Zdravković, M. DHS Substation Data. 2021. Available online: (accessed on 30 April 2021).
  51. Koskela, T.; Kassinen, O.; Harjula, E.; Ylianttila, M. P2P Group Management Systems: A Conceptual Analysis. ACM Comput. Surv. 2013, 45, 1–25. [Google Scholar] [CrossRef]
  52. Orsini, L.; Kessler, S.; Wei, J.; Field, H. How the Brooklyn Microgrid and Transactive Grid Are Paving the Way to Next-Gen Energy Market. In The Energy Internet: An Open Energy Platform to Transform Legacy Power Systems into Open Innovation and Global Economic Engines; Su, W., Huang, A.Q., Eds.; Woodhead Publishing Series in Energy, Woodhead Publishing is an imprint of Elsevier: Duxford, UK; Cambridge, MA, USA, 2019. [Google Scholar]
  53. Soto, E.A.; Bosman, L.B.; Wollega, E.; Leon-Salas, W.D. Peer-to-Peer Energy Trading: A Review of the Literature. Appl. Energy 2020, 116268. [Google Scholar] [CrossRef]
  54. ME Solshare. Available online: (accessed on 30 April 2021).
  55. SOLshare-What We Do. Available online: (accessed on 23 February 2021).
  56. Meet SOLshare, Pioneers in Peer-to-Peer Solar Micro-Grid Technology; Future Energy Ventures, SOLshare: Berlin, Germany, 2020.
  57. Piclo-Building Software for a Smarter Energy Future. Available online: (accessed on 30 April 2021).
  58. Zhang, C.; Wu, J.; Long, C.; Cheng, M. Review of Existing Peer-to-Peer Energy Trading Projects. Energy Procedia 2017, 105, 2563–2568. [Google Scholar] [CrossRef]
  59. Hashcash.Org. Available online: (accessed on 30 April 2021).
  60. Omega Grid: Blockchain Energy Rewards Platform. Available online: (accessed on 30 April 2021).
  61. Pebbles Projekt. Available online: (accessed on 23 February 2021).
  62. Kang, J.; Yu, R.; Huang, X.; Maharjan, S.; Zhang, Y.; Hossain, E. Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains. IEEE Trans. Ind. Inform. 2017, 13, 3154–3164. [Google Scholar] [CrossRef]
  63. Experience & Expertise, Smile Capital. 2014. Available online: (accessed on 30 April 2021).
  64. SolarShare-Start Your Journey To Zero-Carbon Living! SolarShare Limited. Available online: (accessed on 30 April 2021).
  65. sonnenCommunity. Available online: (accessed on 30 April 2021).
  66. Duurzame Energie van Nderlandse Bodem. Available online: (accessed on 30 April 2021).
  67. Zhang, C. Peer-to-Peer Energy Trading in Electrical Distribution Networks. Ph.D. Thesis, Cardiff University, Cardiff, UK, 2017. [Google Scholar]
  68. Zhang, C.; Wu, J.; Zhou, Y.; Cheng, M.; Long, C. Peer-to-Peer Energy Trading in a Microgrid. Appl. Energy 2018, 220, 1–12. [Google Scholar] [CrossRef]
  69. Energy Web. Available online: (accessed on 30 April 2021).
  70. Gavhane, N.; Sefat, M.H.; Hartnett, S.; Kok, W.; Miller, D.; Morris, J.; Pavlovic, M.; Roon, M.; Ruslanova, M. EW-DOS: The Energy Web Decentralized Operating System; Energy Web: Zug, Switzerland, 2020. [Google Scholar]
  71. Lights Out for Yeloha-Why We Shut Down the Solar Sharing Network. Available online: (accessed on 30 April 2021).
  72. Solar, G. What Is Net Metering? How Does Net Metering Work? 2019. Available online: (accessed on 30 April 2021).
  73. Mullendore, S.; Robinson, M.; Jacob, B.; Bowen, L.; Robbins, S. Resilient-Southeast-Charleston; Clean Energy Group: Montpelier, VT, USA, 2019. [Google Scholar]
  74. South Carolina Unanimously Passes Solar Bill to Lift 2% Net Metering Cap. Available online: (accessed on 30 April 2021).
  75. German Government Coalition Agreement Removes Key Hurdles to Renewables Rollout. Available online: (accessed on 30 April 2021).
  76. Insights, L. Power Ledger Starts Another Blockchain Energy Trading Trial in Japan. 2019. Available online: (accessed on 30 April 2021).
  77. Insights, L. Ricoh to Develop Blockchain-Enabled Energy Tracking System to Increase Use of Renewables. 2020. Available online: (accessed on 30 April 2021).
  78. Germany’s Blockchain Initiative: How Adoption Became a Reality in 2020. Available online: (accessed on 30 April 2021).
Figure 1. An ontology-based data integration and standardisation scheme to tackle diverse data sources and formats [42].
Figure 1. An ontology-based data integration and standardisation scheme to tackle diverse data sources and formats [42].
Energies 14 03851 g001
Table 1. IoT temperature sample data [48]. Temperature readings on enterprise building room, both inside and outside, recorded at random intervals (one sample per second).
Table 1. IoT temperature sample data [48]. Temperature readings on enterprise building room, both inside and outside, recorded at random intervals (one sample per second).
IDRoom IDDateTempIn/Out
Unique Reading IDsLocationDate/TimeReadingsInside or Outside
export.temp_log_196134_bd201015Room Admin8 December 2018 09:3029In
export.temp_log_196131_7bca51bcRoom Admin8 December 2018 09:3029In
export.temp_log_196127_522915e3Room Admin8 December 2018 09:2941Out
export.temp_log_196128_be0919cfRoom Admin8 December 2018 09:2941Out
Table 2. National Renewable Energy Laboratory, Department of Energy’s Open Energy Data Initiative (OEDI) [49]. The dataset consists of 1 million solar cell systems along with 78 columns of information on each system.
Table 2. National Renewable Energy Laboratory, Department of Energy’s Open Energy Data Initiative (OEDI) [49]. The dataset consists of 1 million solar cell systems along with 78 columns of information on each system.
ProviderDateSize DCZip CodeUtilityManufacturerEfficiencyLoading Rt
Arkansas State Energy Office29 April 20102.01671953SWEPCOSharp (Osaka, Japan)0.1424305781.178947368
CA_CPUC6 December 20198.8895762Pacific Gas and ElectricLG Electronics Inc. (Seoul, Korea)0.2211225861.113661945
CA_CPUC7 November 20186.795409Pacific Gas and ElectricLG Electronics Inc.0.2031898991.108170691
Table 3. Historical data for District Heating System substation operation for three seasons [50]. The dataset includes: Outside air temperature (C, tsp), water temperature in the secondary supply line (C, tns), water temperature in the secondary return line (C, tps), water temperature in the primary supply line (C, tns), water temperature in the primary return line (C, tps) and heat energy transmitted (Kw, qizm).
Table 3. Historical data for District Heating System substation operation for three seasons [50]. The dataset includes: Outside air temperature (C, tsp), water temperature in the secondary supply line (C, tns), water temperature in the secondary return line (C, tps), water temperature in the primary supply line (C, tns), water temperature in the primary return line (C, tps) and heat energy transmitted (Kw, qizm).
2 October 2017 9:00547464284.543.167.4994179
2 October 2017 10:0054748418143.267.6787167
2 October 2017 11:00547464177.543.167.8455185
2 October 2017 12:00547464175.24368.035181
2 October 2017 13:00646504180.544.168.2119
2 October 2017 15:00646464179.542.268.5645179
2 October 2017 16:00547484181.34468.7437176
2 October 2017 17:00547474183.842.768.9196196
2 October 2017 18:00448484383.243.569.1155176
2 October 2017 19:00448484282.843.169.2916185
2 October 2017 20:00448484381.843.569.4765136
Table 4. P2P projects and companies.
Table 4. P2P projects and companies.
HashCash [59]Using smart contracts to trade solar energyGermany
Omega Grid [60]Solar generation, with interest in EV chargingUSA
Pebbles [61]Optimising virtual power plants, with cloud based services to develop a local P2P blockchain solutionGermany
PETCON [62]Hybrid vehicles in smart grids using P2P blockchainUSA
PICLO [57]Britain’s first peer-to-peer renewable energy trading platformUK
Smile [63]Various energies3 European Islands
SolarShare [64]Singapore’s first solar trading platformSingapore
SolShare [54]Allowing rural communities to utilise solar through P2PBangledsesh
SonnenCommunity [65]Subscription-based service, using solar and battery storageGermany, Austria, Switzerland and Italy
TransActive GridP2P community project, L03 pilot project, The Brooklyn MicrogridUSA
Vandebron [66]Sells renewable energy and also natural gasNetherlands
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O’Regan, B.; Silva, F.; O’Leidhin, E.; Tahir, F.; Mould, K.; Hayes, B.; Hosseinnezhad, V.; Chitchyan, R.; Lyons, P. P2P, CSC and TE: A Survey on Hardware, Software and Data. Energies 2021, 14, 3851.

AMA Style

O’Regan B, Silva F, O’Leidhin E, Tahir F, Mould K, Hayes B, Hosseinnezhad V, Chitchyan R, Lyons P. P2P, CSC and TE: A Survey on Hardware, Software and Data. Energies. 2021; 14(13):3851.

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O’Regan, Brian, Fabio Silva, Eoin O’Leidhin, Farah Tahir, Karen Mould, Barry Hayes, Vahid Hosseinnezhad, Ruzanna Chitchyan, and Padraig Lyons. 2021. "P2P, CSC and TE: A Survey on Hardware, Software and Data" Energies 14, no. 13: 3851.

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