Improved Short-Term Load Forecasting Based on  Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Hernández, Leonardo; Baladrón, Carlos; Aguiar, Javier M.; Calavia, Lorena; Carro, Belén; Sánchez-Esguevillas, Antonio; Sanjuán, Javier; González, Álvaro; Lloret, Jaime

doi:10.3390/en6094489

Cited by 38 publications

(29 citation statements)

References 20 publications

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“…In order to forecast the quantity and direction of power flow in the connecting line, it is necessary for the MGO to know forecasted values for electricity demands of buses and RDG outputs in the MG network. Various artificial intelligence-based forecasting techniques can be effectively used to obtain more accurate forecasting data in a MG environment [16,17].…”

Section: Microgrid Operators As Market Participantmentioning

confidence: 99%

Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

Kim

Kim²,

Jin

et al. 2016

Energies

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Active Network Management (ANM) enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG) and Battery Energy Storage System (BESS) units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.

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Section: Microgrid Operators As Market Participantmentioning

confidence: 99%

Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

Kim

Kim²,

Jin

et al. 2016

Energies

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“…In a DPS, load elasticity E can be defined in (4). Considering that the change of DLMP is small in a short-time period, we assume that the load response remains linear.…”

Section: B Price Elasticity Improvement Descriptionmentioning

confidence: 99%

“…Considering that the change of DLMP is small in a short-time period, we assume that the load response remains linear. For a given value of E, the model creates a load response to a change in the DLMP according to (4). In this paper, both the SE and the CE of load response are considered and the combination of them can be defined in (5).…”

Section: B Price Elasticity Improvement Descriptionmentioning

confidence: 99%

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Wang

Ouyang

Krishnan

et al. 2015

IEEE Trans. Ind. Inf.

135

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Abstract-Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

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“…Hernández et al [13] developed a next day demand profile forecast system for a micro grid by the use of a two stage system. The first stage is comprised by a series of NNs which forecasts demand profile properties such as peak loads and valley loads.…”

Section: Representative Publicationsmentioning

confidence: 99%

“…The DER system incorporates solar photovoltaic (PV) generation and battery energy storage (BES). To adequately schedule DER, information such as the total amount of energy used in a day, magnitude of peak demand can be used to construct demand profiles for future days [13]. Using concepts from Espinoza et al [14], a pattern recognition based expert system will incorporate forecasts of total energy use and peak demand in order to forecast future demand profiles.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Bennett

Stewart

2014

Energies

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This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R 2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.

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Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Cited by 38 publications

References 20 publications

Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

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