14 research outputs found
Optimal energy management of a microgrid system
Mestrado de dupla diplomação com École Superieure en Sciences AppliquéesA smart management strategy for the energy
ows circulating in microgrids is necessary to
economically manage local production and consumption while maintaining the balance between
supply and demand. Finding the optimum set-points of the various generators and the best
scheduling of the microgrid generators can lead to moderate and judicious use of the powers
available in the microgrid. This thesis aims to apply an energy management system based on
optimization algorithms to ensure the optimal control of microgrids by taking as main purpose
the minimization of the energy costs and reduction of the gas emissions rate responsible for
greenhouse gases. Two approaches have been proposed to nd the optimal operating setpoints.
The rst one is based on a uni-objective optimization approach in which several energy
management systems are implemented for three case studies. This rst approach treats the
optimization problem in a uni-objective way where the two functions price and gas emission
are treated separately through optimization algorithms. In this approach the used methods
are simplex method, particle swarm optimization, genetic algorithm and a hybrid method (LPPSO).
The second situation is based on a multiobjective optimization approach that deals with
the optimization of the two functions: cost and gas emission simultaneously, the optimization
algorithm used for this purpose is Pareto-search. The resulting Pareto optimal points represent
di erent scheduling scenarios of the microgrid system.Uma estrat egia de gest~ao inteligente dos
uxos de energia que circulam numa microrrede e
necess aria para gerir economicamente a produ c~ao e o consumo local, mantendo o equil brio
entre a oferta e a procura. Encontrar a melhor programa c~ao dos geradores de microrrede pode
levar a uma utiliza c~ao moderada e criteriosa das pot^encias dispon veis na microrrede. Esta tese
visa desenvolver um sistema de gest~ao de energia baseado em algoritmos de otimiza c~ao para
assegurar o controlo otimo das microrredes, tendo como objetivo principal a minimiza c~ao dos
custos energ eticos e a redu c~ao da taxa de emiss~ao de gases respons aveis pelo com efeito de estufa.
Foram propostas duas estrat egias para encontrar o escalonamento otimo para funcionamento. A
primeira baseia-se numa abordagem de otimiza c~ao uni-objetivo no qual v arios sistemas de gest~ao
de energia s~ao implementados para tr^es casos de estudo. Neste caso o problema de otimiza c~ao
e baseado na fun c~ao pre co e na fun c~ao emiss~ao de gases. Os m etodos de otimiza c~ao utilizados
foram: algoritmo simplex, algoritmos gen eticos, particle swarm optimization e m etodo h brido
(LP-PSO). A segunda situa c~ao baseia-se numa abordagem de otimiza c~ao multi-objetivo que
trata a otimiza c~ao das duas fun c~oes: custo e emiss~ao de gases em simult^aneo. O algoritmo
de otimiza c~ao utilizado para este m foi a Procura de Pareto. Os pontos otimos de Pareto
resultantes representam diferentes cen arios de programa c~ao do sistema de microrrede
Optimization methods for energy management in a microgrid system considering wind uncertainty data
Energy management in the microgrid system is generally formulated as an optimization problem. This paper focuses on the design of a distributed energy management system for the optimal operation of
the microgrid using linear and nonlinear optimization methods. Energy
management is defined as an optimal scheduling power flow problem.
Furthermore, a technical-economic and environmental study is adopted
to illustrate the impact of energy exchange between the microgrid and
the main grid by applying two management scenarios. Nevertheless, the
fluctuating effect of renewable resources especially wind, makes optimal
scheduling difficult. To increase the results reliability of the energy management system, a wind forecasting model based on the artificial intelligence of neural networks is proposed. The simulation results showed the
reliability of the forecasting model as well as the comparison between
the accuracy of optimization methods to choose the most appropriate
algorithm that ensures optimal scheduling of the microgrid generators
in the two proposed energy management scenarios allowing to prove the
interest of the bi-directionality between the microgrid and the main grid.info:eu-repo/semantics/publishedVersio
Combined optimization and regression machine learning for solar Irradiation and wind speed forecasting
Prediction of solar irradiation and wind speed are essential
for enhancing the renewable energy integration into the existing power
system grids. However, the deficiencies caused to the network operations
provided by their intermittent effects need to be investigated. Regarding
reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator.
This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.info:eu-repo/semantics/publishedVersio
An innovative optimization approach for energy management of a microgrid system
The local association of electrical generator including
renewable energies and storage technologies approximately
installed to the client made way for a small-scale power grid
called a microgrid. In certain cases, the random nature of
renewable energy sources, combined with the variable pattern
of demand, results in issues concerning the sustainability and
reliability of the microgrid system. Furthermore, the cost of
the energy coming from conventional sources is considering as
matter to the private consumer due to its high fees. An improved
methodology combining the simplex-based linear programming
with the particle swarm optimisation approach is employed
to implement an integrated power management system. The
energy scheduling is done by assuming the consumption profile
of a smart city. two scenarios of energy management have
been suggested to illustrate the behaviour of cost and gas
emissions for an optimised energy management. The results
showed the reliability of the energy management system using
an improvemed approach in scheduling of the energy flows for
the microgrid producers, limiting the utility’s cost versus an
experiment that had already been done for a similar system using
the identical data. The outcome of the computation identified
the ideal set points of the power generators in a smart city
supplied by a microgrid, while guaranteeing the comfort of the
customers i.e without intermetency in the supply, also, reducing
the emissions of greenhouse gases and providing an optimal
exploitation cost for all smart city users. Morover, the proposed
energy management system gave an inverse relation between
economic and environmental aspects, in fact, a multi-objective
optimization approach is performed as a continuation of the
work proposed in this paperinfo:eu-repo/semantics/publishedVersio
A short term wind speed forecasting model using artificial neural network and adaptive neuro-fuzzy inference system models
Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.info:eu-repo/semantics/publishedVersio
Optimal Sizing of a Hybrid Energy System Based on Renewable Energy Using Evolutionary Optimization Algorithms
The current trend in energy sustainability and the energy
growing demand have given emergence to distributed hybrid energy systems
based on renewable energy sources. This study proposes a strategy
for the optimal sizing of an autonomous hybrid energy system integrating
a photovoltaic park, a wind energy conversion, a diesel group, and
a storage system. The problem is formulated as a uni-objective function
subjected to economical and technical constraints, combined with
evolutionary approaches mainly particle swarm optimization algorithm
and genetic algorithm to determine the number of installation elements
for a reduced system cost. The computational results have revealed an
optimal configuration for the hybrid energy system.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020.info:eu-repo/semantics/publishedVersio
A statistical estimation of wind data generation in the municipality of Bragança, Portugal
The existing wind energy potential in Portugal
makes way for developing electrical energy in the northern
region. In this work, wind speed data were statistically investigated
using Weibull distribution to identify the characteristics
of converting wind energy in Serra da Nogueira mountain
in the Municipality of Braganca. An hourly wind speed time
series data set from January 2002 to December 2021 have been
exported from OPEN-METEO online platform after reliability
data was proved through a correlation study with real data.
The Weibull parameters including form K and scale C factors,
frequency distribution function f(v), has been used to describe
the best wind distribution. Moreover, statistical estimation of
wind energy potential at different altitudes (10m, 50m, 100m,
150m, and 200m) throughout vertical extrapolation and wind
direction study is performed to identify the suitable high wind
turbine hub. Finally, the evaluation of the predicted electrical
energy produced is done while considering the judicious choice of
the wind turbines and the charge factor. The Weibull parameters,
frequency distribution, wind speed stability, and potentially
provided by this study were motivating results for implementing
wind farm in the mountain of Serra da Nogueira.info:eu-repo/semantics/publishedVersio
A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
This paper analyzes the application of a population-based
algorithm and its improvement in solving an optimal power flow problem.
Simulations were performed on a 14-bus IEEE network modified
to include renewable energy sources-based power plants: a wind park
and two photovoltaic solar parks. In this scenario, the high penetration
of intermittent energy sources in the grid makes it necessary to curtail
active power during peak generation to maintain the balance between
load and generation. However, European energy market regulations limit
the annual curtailment of RES generators and penalize discriminatory
curtailment actions between generators. This work exploits the minimization
of transmission active loss while respecting its security constraints.
Additionally, constraints were introduced in the optimal power flow problem
to mitigate active power curtailment of the renewable source generators
and to secure a non-discriminatory characteristic in curtailment
decisions. The non-convex nature of the problem, intensified by the introduction
of non-linear constraints, suggests the exploitation of heuristic
algorithms to locate the optimal global solution. The obtained results
demonstrate that a hybrid GA algorithm can improve convergence speed,
and it is useful in determining the problem solution in cases where deterministic
algorithms are unable to converge.The authors are grateful to the Foundation for Science
and Technology (FCT, Portugal) for financial support through national funds
FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020),
SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247-
FEDER-072615 EPO - Enline Power Optimization - The supra-grid optimization software.info:eu-repo/semantics/publishedVersio
Optimal energy management of microgrid using multi-objective optimisation approach
The use of several distributed generators as well as the energy storage system in a local microgrid require an energy management system to maximize system efficiency, by managing generation and
loads. The main purpose of this work is to find the optimal set-points
of distributed generators and storage devices of a microgrid, minimizing simultaneously the energy costs and the greenhouse gas emissions. A
multi-objective approach called Pareto-search Algorithm based on direct
multi-search is proposed to ensure optimal management of the microgrid.
According to the non-dominated resulting points, several scenarios are
proposed and compared. The effectiveness of the algorithm is validated,
giving a compromised choice between two criteria: energy cost and GHG
emissions.info:eu-repo/semantics/publishedVersio
Smart microgrid management: a hybrid optimisation approach
The association of distributed generators, energy storage systems
and controllable loads close to the energy consumers gave place to a small-scale
electrical network called microgrid. The stochastic behavior of renewable energy
sources, as well as the demand variation, can lead in some cases to problems
related to the reliability of the microgrid system. On the other hand, the market
price of electricity from mainly non-renewable sources becomes a concern for a
simple consumer due to its high costs.
An innovative optimization method, combining linear programming,
based on the simplex method, with the particle swarm optimisation algorithm is
used to develop an energy management system. The management is performed
considering a smart city’s consumption profile, two management scenarios have
been proposed to characterize the relation price versus gas emissions for optimal
energy management.
The simulation results have demonstrated the reliability of the
optimisation approach on the energy management system in the optimal
scheduling of the microgrid generators power flows, having achieved a better
energy price compared to a previous study with the same data. The
computational results identified the optimal set-points of generators in a smart
city supplied by a microgrid while ensuring consumer comfort, minimising
greenhouse gas emissions and guarantee an appropriate operating price for all
consumers in the smart city.
The energy management system based on the proposed
optimisation approach gave an inverse correlation between economic and
environmental aspects, in fact, a multi-objective optimisation approach is
performed as a continuation of the work proposed in this paper.This work has been supported by Fundação La Caixa and FCT — Fundação para a Ciência e Tecnologia within the
Project Scope: UIDB/05757/2020info:eu-repo/semantics/publishedVersio