Dejan Ilic

An energy market for trading electricity in smart grid neighbourhoods

The smart grid vision relies on active interaction with all of its stakeholders. As consumers are acquiring energy generation capabilities, hence becoming prosumers (producers and consumers), a meaningful way to interact among them would be to trade over a marketplace. Market-driven interactions have been proposed as a promising potential interaction method due to the monetary incentives and other benefits involved for the participants [1]. In the Internet era an on-line marketplace is an thriving concept as it overcomes potential accessibility issues, however it is not clear how they should be structured, operated, what their limits and benefits might be. The design, implementation, modus-operandi as well as the assessment of such an energy market place for smart grid neighbourhoods is presented.

The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading

Local electricity markets may emerge as a mechanism for managing the increasing numbers of distributed generation resources. However, in order to be successful, these markets will heavily rely on accurate forecasts of consumption and/or production from its participants. This issue has not been widely researched in the context of such markets, and it presents a clear roadblock for wide market adoption as forecasting errors result in penalty and opportunity costs. Forecasting individual demand often leads to large errors. However, these errors can be reduced through the creation of groups, however small. In the work presented here, we investigate the relationship between group size and forecast accuracy, based on Seasonal-Naïve and Holt-Winters algorithms, and the effects forecasting errors have on trading in an intra-day local electricity market composed of consumers and “prosumers.” Furthermore, we measure the performance of a group participating on the market, and demonstrate how it can be a mitigating strategy to enable even highly unpredictable individuals to reduce their costs, and participate more effectively in the market.

Energy services for the smart grid city

The smart grid enabled city is an emerging complex system of systems where different stakeholders will have to strive towards achieving their goals while interacting with each-other. At parts of the city such as the districts, the energy signature and efforts towards its better energy efficiency will heavily depend on the utilization of availability and optimal usage of the local resources. The latter may be very dynamic and depend on several complex conditions such as weather, prosumer behavioural patterns, business interactions etc. An emergent city-wide behaviour appears when a number of simple entities i.e. the prosumers, operate in an environment, forming more complex behaviours as a collective. In order to empower the smart grid city, several energy services capturing the common needs of all stakeholders need to be made available to them. As a result of a (potentially) common platform that offers basic energy services, rapid development of applications can be realized without the need to start from scratch. Such energy services have been identified, analysed and implemented, in the context of a wider enterprise system architecture. An insight on their functionality, usage and development challenges and experiences is provided.

Assessment of high-performance smart metering for the web service enabled smart grid era

The electricity network is undergoing a significant change towards a more adaptive, intelligent, self-managing, collaborative and information-driven grid. According to the smart grid vision, any electronic device connected to it will be able to communicate its consumed or produced energy almost in real time. Based on the analysis of this newly acquired information, a new generation of services and decision support systems can be realized, enabling more intelligent decisions, and ultimately a more efficient energy system. Therefore, high-performance acquisition of smart metering information from large scale distributed infrastructures is of key importance for the upcoming Internet-based enterprise services and mash-up applications. We have used open source software to build a web service-based advanced metering infrastructure of simulated smart meters, concentrators, and a smart metering platform, all interconnected via web services. We measure in a methodological fashion the performance of the various components of the architecture and evaluate their limitations. Finally we identify key performance indicators that need to be considered when deploying large-scale smart metering systems, and discuss on challenges and directions that arise.

Impact assessment of smart meter grouping on the accuracy of forecasting algorithms

The increased penetration of smart meters generates huge amounts of fine-grained data, which may empower a new generation of energy related applications and services. Significant research efforts focus on the usage of such data to mainly improve the business processes of the electrical grid operators and provide some value added services to the endusers. Forecasting has a prominent position as it is a crucial planning step, and is mostly used to predict the grid load through highly-aggregated data. However, with the dramatic increase on fine-grained data, new challenges arise as forecasting can now also be done on much shorter and detailed time-series data, which might provide new insights for future applications and services. For the smart grid era, being able to segment customers on highly predictable groups or identify highly volatile ones, is a key business advantage as more targeted offers can be made. This work focuses on the analysis and impact assessment of in the context of smart metering data aggregation. A system to measure the impact of aggregation is designed and its performance is assessed. We experiment with measuring of the forecast accuracy on various levels of individual load aggregation, and investigate the identification of highly predictable groups.

Using flexible energy infrastructures for demand response in a Smart Grid city

The emerging infrastructure of the Smart Grid, and the multitude of the new energy services it will offer, is expected to radically affect business relationships among its stakeholders. Beyond enhancements in existing processes, innovative approaches will be made possible by relying on a fine-grained monitoring and control capabilities over modern informationcentric infrastructure. Traditional infrastructure owners will be able to take advantage of the new capabilities in order to not only better manage their costs, but also potentially increase their revenue by tapping into their flexibility of adjusting energy behaviour. The latter is of growing interest to, for instance, facility managers of municipalities who are reassessing cost-benefit issues for their infrastructures which include buildings, offices, arenas, schools, convention centres, shopping complexes, hospitals, hotels, and among other things the public lighting system. A closer look is taken on how flexible prosumer infrastructures may interact with the Smart Grid and how new revenue may be generated. The use case of using the capability-constrained public lighting system (PLS) flexibility as a new revenue source is analysed.

Using a 6LoWPAN smart meter mesh network for event-driven monitoring of power quality

Power quality monitoring is one of the key issues of managing an electrical grid, which is becoming even more important with more distributed and more variable generation. Today expensive equipment allows monitoring of the power network at key points, but for cost reasons this can not reach the residential end-user. To prevent an excessive need for specialized monitoring hardware, e.g. network analysers, it is proposed to engage the capabilities of modern smart meters which can monitor and report power quality events (e.g. voltage deviations). Subsequently a grid operator can follow up with actions in an affected area in order to analyse problems e.g. by increasing the sampling rate. Although the smart meter precision is not comparable to the precision of a commercial network analyser, in large numbers distributed smart meters forming a mesh network can provide sufficient information for power quality in an area while keeping the monitoring overhead and the cost low. It is shown that by using modern interoperable wireless communication protocols and Internet services, the proposed system has a high degree of flexibility, and good potential for scalability and resilience. The preliminary evaluation shows that the smart metering infrastructure, if coupled with suitable information and communication tools, can offer innovative value-added services and enhance existing business processes.

Sensing in power distribution networks via large numbers of smart meters

The electricity grid is undergoing significant changes as it paves its way towards a smarter grid. New technologies provide a much clearer view on the electricity grid and its processes, mainly due to the fine-grained and near real-time acquisition of data as well as assessment of it. The huge amount of information will include additional technical data beyond the traditional metering information for billing purposes. This may give rise to a new generation of tools that rigorously monitor residential endpoints. Such monitoring may assist towards rapidly identifying problems and decrease the time to resolve them. Currently, a real-time view of the grid state is done by specialized equipment, but their costs combined with their need to be widely deployed, is a limiting factor. Todays modern communication approaches coupled with high-performance analytics can be used in complex system monitoring and assessment. We argue how a large network of distributed smart meters can be used for analytics of the smart grid, based on our experiences in developing a smart metering platform that is currently monitoring approximately 5000 real smart meters. We analyse and discuss the results stemming from the data being streamed to the platform, and show how new insights can be obtained with respect to power quality.

Evaluation of the scalability of an energy market for Smart Grid neighborhoods

The electric power grid is undergoing fundamental changes in light of the current focus on distributed generation, and in particular renewable generation (e.g., solar and wind). As a result, new methodologies and technologies are needed to effectively coordinate and make optimal use of the these resources. A distribution-system level energy market offers the potential to address this issue by providing an efficient mechanism for the pricing and allocation of resources. Market participants (e.g., households, ESCos, asset managers etc.) can apply economically driven strategies to trade energy while reacting to current and local levels of production and consumption. We evaluate here such a local neighborhood market and investigate its scalability under different numbers of participants and different penetrations photo-voltaic (PV) generation. The evaluation is carried out by simulating market operations under realistic production and consumption conditions. Results showed that the proposed market model scales well against both parameters.

A comparative analysis of smart metering data aggregation performance

In the Smart Grid era fine-grained energy information pertaining real world processes can be collected and may reveal new insights if these can be analyzed in real-time. Energy “Big Data” analytics can lead to a plethora of new innovative applications and enhance decision making processes. However, to do so, we need new enterprise tools and approaches that can take into consideration the specifics of the energy domain and offer high performance analytics on its raw data. In this work, experiments are conducted to measure the performance of the different levels of energy data aggregation. Thousands of smart meters are aggregated, by usage of the collected energy readings from a real-world trial. Using a selected dataset, the traditional database system (row-based) performance is compared to the emerging column-based approach in order to assess the suitability for real-time analytics in such scenarios.