Colin Harris

An empirical study of the potential for context-aware power management

Context-aware power management (CAPM) uses context (e.g., user location) likely to be available in future ubiquitous computing environments, to effectively power manage a buildings energy consuming devices. The objective of CAPM is to minimise overall energy consumption while maintaining user-perceived device performance. The principal context required by CAPM is when the user is NOT USING and when the user is about to use a device. Accurately inferring this user context is challenging and there is a balance between how much energy additional context can save and how much it will cost energy wise. This paper presents results from a detailed user study that investigated the potential of such CAPM. The results show that CAPM is a hard problem. It is possible to get within 6% of the optimal policy, but policy performance is very dependent on user behaviour. Furthermore, adding more sensors to improve context inference can actually increase overall energy consumption.

Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods

Applications such as generator scheduling, household smart device scheduling, transmission line overload management and microgrid islanding autonomy all play key roles in the smart grid ecosystem. Management of these applications could benefit from short-term load prediction, which has been successfully achieved on large-scale systems such as national grids. However, the scale of the data for analysis is much smaller, similar to the load of a single transformer, making prediction difficult. This paper examines several prediction approaches for day and week ahead electrical load of a community of houses that are supplied by a common residential transformer, in particular: artificial neural networks; fuzzy logic; auto-regression; auto-regressive moving average; auto-regressive integrated moving average; and wavelet neural networks. In our evaluation, the methods use pre-recorded electrical load data with added weather information. Data is recorded from a smart-meter trial that took place during 2009-2010 in Ireland, which registered individual household consumption for 17 months. Two different scenarios are investigated, one with 90 houses, and another with 230 houses. Results for the two scenarios are compared and the performances of the evaluated prediction methods are discussed.

A hybrid approach to very small scale electrical demand forecasting

Microgrid management and scheduling can considerably benefit from day-ahead demand forecasting. Until now, most of the research in the field of electrical demand forecasting has been done on large-scale systems, such as national or municipal level grids. This paper examines a hybrid method that attempts to accurately estimate day-ahead electrical demand of a small community of houses resembling the load of a single transformer, the equivalent sizing of a small virtual power plant or microgrid. We have combined the advantages of several forecasting methods into a novel hybrid approach: artificial neural networks, fuzzy logic, auto-regressive moving average and wavelet smoothing. The combined system has been tested over two different scenarios, comprising communities of 90 houses and 230 houses, sampled from a smart-meter field trial in Ireland. Our hybrid approach achieves results of 3.22% NRMSE and 2.39% NRMSE respectively, leading to general improvements of 11%-28% when compared to the individual methods.

A dynamic forecasting method for small scale residential electrical demand

Small scale electrical demand forecasting is an emerging field motivated by the penetration of renewable energy sources and the growth of microgrids and virtual power plants. These advances pose more complex forecasting challenges compared to the already established large scale forecasting approaches. Current short term load forecasting methods deal with two types of day, normal and anomalous, which are predicted separately. Anomalous days are classified as such ahead of time, based on key calendar events such as public holidays. However, there are some anomalous days which are not always predictable on a day ahead basis. Due to unforeseen events, a seemingly normal day can progress towards an anomalous case causing high errors in prediction. We propose a new dynamic forecasting mechanism that actively monitors residential electrical demand along a forecasted day, and detects anomalous pattern changes from a previously predicted demand of the day. A self-organising map is employed to detect anomalous days as they progress. Once an anomaly is detected, a neural network based prediction system changes its input neurons according to a previously detected and recorded match found in a database of anomalous days, in order to accommodate the anomalous day prediction. Results are based on measured power demands recorded in Ireland from domestic smart-meters between 2009-2011, and focus on small scale residential electrical demands of up to 350 kWh. During anomalous days our dynamic prediction approach achieves forecasting results within 3.63% of the real load, down from the 7.37% obtained by the initial prediction algorithm and the 5.41% achieved by standalone re-prediction, without pattern matching.

Reducing electricity costs in a dynamic pricing environment

Smart Grid technologies are becoming increasingly dynamic, so the use of computational intelligence is becoming more and more common to support the grid to automatically and intelligently respond to certain requests (e.g., reducing electricity costs giving a pricing history). In this work, we propose the use of a particular computational intelligence approach, denominated Distributed W-Learning, that aims to reduce electricity costs in a dynamic environment (e.g., changing prices over a period of time) by turning electric devices on (i.e., clothes dryer, electric vehicle) at residential level, at times when the electricity price is the lowest, while also, balancing the use of energy by avoiding turning on the devices at the same time. We make this problem as realistic as possible, by considering the use of real-world constraints (e.g., time to complete a task, boundary times within which a device can be used). Our results clearly indicate that the use of computational intelligence can be beneficial in this type of dynamic and complex problems.

Set point control for charging of electric vehicles on the distribution network

Many countries envisage a future where renewable electricity will be the predominant energy source. For example, Irelands smart grid roadmap has targets of 40% of electricity from renewables by 2020 and 80% by 2050. To achieve these targets will require new ways of operating the grid. We propose that there will be two types of demand, a load which can be influenced by dynamic pricing and a more tightly controlled flexible load that can be used to shape the aggregate demand. Key examples of this flexible load are electric vehicles (EVs), electric storage heating and hot water heating. This paper explores two algorithms that implement tight set point control for a set of EVs on a distribution feeder line. The first algorithm uses a variable power charger for charging the EVs and the second algorithm shows that it is possible to achieve similar results with a much simpler on-off charger.

Autonomous Demand-Side Management system based on Monte Carlo Tree Search

Smart Grid (SG) technologies are becoming increasingly dynamic, motivating the use of computational intelligence to support the SG by predicting and intelligently responding to certain requests (e.g, reducing electricity costs given fluctuating prices). The presented work intends to do precisely this, to make intelligent decisions to switch on electric devices at times when the electricity price (prices that change over time) is the lowest while at the same time attempting to balance energy usage by avoiding turning on multiple devices at the same time, whenever possible. To this end, we use Monte Carlo Tree Search (MCTS), a real-time decision algorithm. MCTS takes into consideration what might happen in the future by approximating what other entities/agents (electric devices) might do via Monte Carlo simulations. We propose two variants of this method: (a) maxn MCTS approach where the competition for resources (e.g, lowest electricity price) happens in one single decision tree and where all the devices are considered, and (b) two-agent MCTS approach, where the competition for resources is distributed among various decision trees. To validate our results, we used two scenarios, a rather simple one where there are no constraints associated to the problem, and another more complex, and realistic scenario with equality and inequality constraints associated to the problem. The results achieved by this real-time decision tree algorithm are very promising, specially those achieved by the maxn MCTS approach.

A practical approach to investigating energy consumption of industrial compressed air systems

While there are several best practice standards available for minimizing the energy requirement for compressed air use in an industrial context, moving to best practice often requires investment and operational change. In production facilities, there is often a reluctance to commit to this type of change without a clear view of the benefit. Furthermore, there is very little detailed information available in the open literature that allows even a qualitative assessment of priorities. In order to address this shortcoming, a practical approach is proposed to provide detailed compressed air consumption information throughout an industrial site. The energy of the compressed air is evaluated at each key element of the system and the typical end-use application profile assessed. Simple models of the consumption rates are used to relate duty cycle and device count with actual total consumption. This approach is complemented with a novel method of assessing the leak rate from the entire system, based on the pressure decay time. The method, referred to as the ‘end use catalogue’ has been demonstrated at a manufacturing site with a wide range of compressed air applications. The model has been used to identify the most significant energy-intensive compressed air applications and possible strategies to reduce the energy requirement. In the particular site used as a demonstration, it was found that open blowing operations (e.g. fluidizing) are the largest consumers of compressed air which are amenable to intervention. System leakage accounts for almost 21 per cent of the compressed air generated, representing an energy input of 432 kWh per day. It is concluded that this approach can help to identify priorities for optimizing compressed air use at an industrial site without compromising the production yield.

A distributed agent based mechanism for shaping of aggregate demand on the smart grid

For electrical grid systems with significant levels of intermittent renewables it will be essential to shape aggregate demand to match periods of cheap renewable supply. For example, the Irish grid will have approximately 40% of its electricity coming from intermittent wind turbines by 2020. Currently at 18%, the turbines are curtailed when they reach 50% of instantaneous supply for control reasons. This could be avoided if the aggregate demand could be shaped to follow these periods of high renewable supply. This paper develops a distributed agent based mechanism for shaping of aggregate demand on the smart grid. Our previous work developed two set point control algorithms that a transformer agent implements to keep the aggregate demand from going above the maximum limit of the transformer. We now extend this to enable the transformer agent to shape the aggregate demand over the 24 hour period. Since the demand is now constrained to a given shape, we must ensure the utility of the devices being charged. We develop an urgency protocol with inherent backoff that each device agent implements to guarantee the utility of its device. Finally, we develop a method for the transformer agent to determine the bounds of shape that the network will tolerate.

Industrial Compressed Air Use: Two Case Studies

While there are several best practice standards available for minimizing the energy requirement for compressed air use in an industrial context, moving to best practice often requires investment and operational change. In production facilities, there is often a reluctance to commit to this type of change without a clear view of the benefit. Furthermore, there is very little detailed information available in the open literature that allows even a qualitative assessment of priorities. In order to address this shortcoming, analyses of two industrial compressed air systems which are already installed in manufacturing plants have been conducted in the context of energy usage. The installations are quite different in compressed air needs: one is focused on actuation and drying; while the other uses compressed air primarily for material handling. In both sites, the energy of the compressed air is evaluated at each key element of the system and the typical end use application profile is assessed. Simple models of the consumption rates are used to relate duty cycle and device count with actual total consumption. A new way of assessing the leak rate from the entire system has been developed, based on the pressure decay time, and has been implemented at one site. In this way, the energy balance of the system entire has been analyzed quantitatively, with the effect of distribution leaks accounted for directly. It is found that in both sites, open blowing operations (e.g. drying) are the largest, consumers which are amenable to optimization. It is also found that the measured leak rate at one site represented 23% of the compressed air generated, with an energy input of 455kWh per day. It is concluded that this approach can help to identify priorities for optimizing CA use at an industrial site.Copyright