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

  • Gene Fe Palencia

    Student thesis: Doctoral ThesisDoctor of Philosophy


    For microgrids with limited generation capacity, allocating a daily
    equal energy budget to each household is one way of ensuring that
    all households are provided with sufficient energy to be used in a
    day without compromising the daily operation of the power system.
    With the same daily energy quota, households are given freedom on
    how to spend energy according to their preferences and priorities.
    Issues in this type of energy management scheme include 1) power
    outage in households that use up all their energy allowance before
    the scheduled replenishment and 2) unused energy allocation turned
    to waste from households that are unable to consume the energy
    allowance. Energy waste in terms of unused energy allocation of some
    households can be beneficial to other households. The unused energy
    can be distributed to other households that experience a power outage
    and need more energy than the allocated. One approach to solving the
    above issues is to frame the problem as an optimisation problem that
    aims to minimise the energy wastage and maximise energy availability.
    This research proposes an optimal energy allocation for households
    connected to generation-constrained microgrids. The proposed optimal
    energy allocation scheme has two main parts. First, the ideal
    energy utilisation of each household is predicted using a multi-layer
    perceptron (MLP); secondly, the optimal energy allocations for the
    households based on their predicted utilisations are derived using
    Karush-Kuhn-Tucker (KKT) optimality conditions. From the application
    of the KKT conditions, a methodology for optimal energy allocation
    that is adaptive to each household is proposed. The approach is
    optimal as it minimises the energy wastage/deficit while maximising
    energy availability to households, and adaptive because it uses the
    household’s historical data and demographic information.
    To support the development of the MLP-based forecast model, this
    thesis implemented an energy monitoring system called Philippines
    Micro-Off-Grids (PMOG) system to gather the actual historical energy
    usage 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 access
    to villages that are not accessible by the traditional grid. There are
    three villages selected with two of them being off-grid communities
    and one being grid-tied community. The energy data from PMOG
    system serves as the baseline data for the development of the forecast
    model and the optimal allocation scheme. A survey is also conducted
    to gather the household demographic information that affects their
    daily energy consumption.
    This thesis presents an experimental method to determine the best
    combination of hidden layers and neurons of the neural network along
    with the input delay window in shaping the input variables that allows
    the forecast model to generate the lowest possible root mean squared
    error (RMSE). Since the optimal energy allocation is dependent on the
    accuracy of MLP-based load forecast model, the right combination of
    those design parameters of the neural network together with the delay
    window used in shaping the inputs is crucial. These parameters are
    considered as the main factors affecting the performance of the neural
    network in forecasting.
    Experimental results show that as the households’ demographic
    information is included as input variables secondary to the historical
    energy data and weather information, the performance of the neural
    network improves significantly. The RMSE decreases from 92 W to 81
    W, which represents a 12 % decrease for a neural network with three
    hidden layers, 20 neurons and seven delays.
    Given the limited generation capacity of the microgrid, the objective
    is to minimise the squared difference between the ideal utilisation of
    the household (which is estimated by the MLP-based forecast model)
    and the (calculated) allocated energy. Results from the data from
    the select villages show an aggregated unused or deficit energy per
    household (for a day) from the existing equal allocation can be reduced
    from 0.24 kWh to 0.11 kWh using the proposed dynamic/adaptive
    allocation, which is about 54 % reduction in unused or deficit energy.
    For 288 days, a total of 44 % reduction of energy wastage is achieved
    with the proposed methodology when compared with equal allocation,
    that is 112 kWh using equal energy allocation, and 62 kWh using the
    proposed optimal energy allocation.
    In summary, the proposed approach of allocating the daily energy
    allowance of the household which is a hybrid approach using an MLPbased
    forecast model and KKT optimality conditions minimises the
    unused energy and enables households to maximise their energy usage
    without compromising the minimum energy requirement of each
    household in villages powered by microgrids with limited generation
    capacity. By incorporating household profiles as inputs to the MLPbased
    forecast model, prediction accuracy was improved by 12% in
    terms of RMSE. From my experiments, employing MLP-based forecast
    model ensures better forecasting performance than other techniques
    such as Autoregressive Integrated Moving Average (ARIMA), Radial
    Basis Function Network (RBFN) and Gaussian Process Regression
    (GPR). The overall average accuracy for the MLP-based forecast model
    is 91% with the highest accuracy of 93% for House 5 predictions, and
    the lowest is 91% for House 3.
    This approach is expected to work on households with similar
    profiles connected to any off-grid power systems. Optimising the daily
    energy quota will enable the village to maximise the usage of the
    available energy with minimum wastage in terms of unused energy
    quota. This approach will also lead the village to have a better payment
    scheme based on their actual usage of electricity.
    Date of Award13 Oct 2022
    Original languageEnglish
    Awarding Institution
    • Coventry University
    • University of San Carlos
    SponsorsBritish Council & Science Education Institute - DOST
    SupervisorJames Brusey (Supervisor), Elena Gaura (Supervisor) & Kojo Sarfo Gyamfi (Supervisor)



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