Palencia, Gene Fe

English

For microgrids with limited generation capacity, allocating a dailyequal energy budget to each household is one way of ensuring thatall households are provided with sufficient energy to be used in aday without compromising the daily operation of the power system.With the same daily energy quota, households are given freedom onhow to spend energy according to their preferences and priorities.Issues in this type of energy management scheme include 1) poweroutage in households that use up all their energy allowance beforethe scheduled replenishment and 2) unused energy allocation turnedto waste from households that are unable to consume the energyallowance. Energy waste in terms of unused energy allocation of somehouseholds can be beneficial to other households. The unused energycan be distributed to other households that experience a power outageand need more energy than the allocated. One approach to solving theabove issues is to frame the problem as an optimisation problem thataims to minimise the energy wastage and maximise energy availability.This research proposes an optimal energy allocation for householdsconnected to generation-constrained microgrids. The proposed optimalenergy allocation scheme has two main parts. First, the idealenergy utilisation of each household is predicted using a multi-layerperceptron (MLP); secondly, the optimal energy allocations for thehouseholds based on their predicted utilisations are derived usingKarush-Kuhn-Tucker (KKT) optimality conditions. From the applicationof the KKT conditions, a methodology for optimal energy allocationthat is adaptive to each household is proposed. The approach isoptimal as it minimises the energy wastage/deficit while maximisingenergy availability to households, and adaptive because it uses thehousehold’s historical data and demographic information.To support the development of the MLP-based forecast model, thisthesis implemented an energy monitoring system called PhilippinesMicro-Off-Grids (PMOG) system to gather the actual historical energyusage data in representative households from select villages in Cebu,Philippines which have microgrids with limited generation capacity.In the Philippines, there are 40 million people without access to electricity[Och13] and microgrids are used to provide electricity accessto villages that are not accessible by the traditional grid. There arethree villages selected with two of them being off-grid communitiesand one being grid-tied community. The energy data from PMOGsystem serves as the baseline data for the development of the forecastmodel and the optimal allocation scheme. A survey is also conductedto gather the household demographic information that affects theirdaily energy consumption.This thesis presents an experimental method to determine the bestcombination of hidden layers and neurons of the neural network alongwith the input delay window in shaping the input variables that allowsthe forecast model to generate the lowest possible root mean squarederror (RMSE). Since the optimal energy allocation is dependent on theaccuracy of MLP-based load forecast model, the right combination ofthose design parameters of the neural network together with the delaywindow used in shaping the inputs is crucial. These parameters areconsidered as the main factors affecting the performance of the neuralnetwork in forecasting.Experimental results show that as the households’ demographicinformation is included as input variables secondary to the historicalenergy data and weather information, the performance of the neuralnetwork improves significantly. The RMSE decreases from 92 W to 81W, which represents a 12 % decrease for a neural network with threehidden layers, 20 neurons and seven delays.Given the limited generation capacity of the microgrid, the objectiveis to minimise the squared difference between the ideal utilisation ofthe household (which is estimated by the MLP-based forecast model)and the (calculated) allocated energy. Results from the data fromthe select villages show an aggregated unused or deficit energy perhousehold (for a day) from the existing equal allocation can be reducedfrom 0.24 kWh to 0.11 kWh using the proposed dynamic/adaptiveallocation, which is about 54 % reduction in unused or deficit energy.For 288 days, a total of 44 % reduction of energy wastage is achievedwith the proposed methodology when compared with equal allocation,that is 112 kWh using equal energy allocation, and 62 kWh using theproposed optimal energy allocation.In summary, the proposed approach of allocating the daily energyallowance of the household which is a hybrid approach using an MLPbasedforecast model and KKT optimality conditions minimises theunused energy and enables households to maximise their energy usagewithout compromising the minimum energy requirement of eachhousehold in villages powered by microgrids with limited generationcapacity. By incorporating household profiles as inputs to the MLPbasedforecast model, prediction accuracy was improved by 12% interms of RMSE. From my experiments, employing MLP-based forecastmodel ensures better forecasting performance than other techniquessuch as Autoregressive Integrated Moving Average (ARIMA), RadialBasis Function Network (RBFN) and Gaussian Process Regression(GPR). The overall average accuracy for the MLP-based forecast modelis 91% with the highest accuracy of 93% for House 5 predictions, andthe lowest is 91% for House 3.This approach is expected to work on households with similarprofiles connected to any off-grid power systems. Optimising the dailyenergy quota will enable the village to maximise the usage of theavailable energy with minimum wastage in terms of unused energyquota. This approach will also lead the village to have a better paymentscheme based on their actual usage of electricity.<br/

Coventry University Pure Portal

Optimal energy allocation for households in generation-constrained off-grid microgrids

https://pure.coventry.ac.uk/ws/files/57467385/GeneThesis_Redacted.pdf

Optimal energy allocation for households in generation-constrained off-grid microgrids

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Coventry University Pure Portal