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A Sequential Linear Programming algorithm for economic optimization of Hybrid Renewable Energy Systems

TLDR
In this paper, an optimization tool for a general hybrid renewable energy system (HRES) is developed: it generates an operating plan over a specified time horizon of the setpoints of each device to meet all electrical and thermal load requirements with possibly minimum operating costs.
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This article is published in Journal of Process Control.The article was published on 2017-10-05 and is currently open access. It has received 33 citations till now. The article focuses on the topics: Renewable energy & Linear programming.

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Citations
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Journal ArticleDOI

A comprehensive review on renewable energy integration for combined heat and power production

TL;DR: The primary objectives of this study are to analyze the performance of most recent state-of-the-art designs, and present a comprehensive review that underlines the current research trends in the field of solar, wind, and geothermal energies.
Journal ArticleDOI

A literature review and statistical analysis of photovoltaic-wind hybrid renewable system research by considering the most relevant 550 articles: An upgradable matrix literature database

TL;DR: The analysis highlights that PV systems are preferred at low installed powers, especially for residential use and stand-alone mode, while wind systems, in addition to being extensively used for low installed power, demonstrates higher employment compared to PV systems as the power increases.
Journal ArticleDOI

Sizing, optimization, control and energy management of hybrid renewable energy system—A review

TL;DR: A deep literature review of the recent paper published in hybrid renewable energy field focuses on four essential categories which is sizing (using software or using traditional methods), optimization, optimization and energy management.
Journal ArticleDOI

A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings

- 01 Jan 2022 - 
TL;DR: In this paper , a distributed energy system (DES), which combines hybrid energy storage into fully utilized renewable energies, is feasible in creating a nearly zero-energy community and buildings, the DES configuration is optimized in relation to its environment, economy, and net interaction.
Journal ArticleDOI

A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings

TL;DR: In this article, a distributed energy system (DES), which combines hybrid energy storage into fully utilized renewable energies, is feasible in creating a nearly zero-energy community, improving the design, optimization, and operation of DESs is conducive to improving system performance.
References
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Book

Numerical Optimization

TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
BookDOI

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

TL;DR: Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.
Journal ArticleDOI

Optimization by Simulated Annealing: Quantitative Studies

TL;DR: Experimental studies of the simulated annealing method are presented and its computational efficiency when applied to graph partitioning and traveling salesman problems are presented.
Journal ArticleDOI

A review of computer tools for analysing the integration of renewable energy into various energy systems

TL;DR: In this paper, a review of the different computer tools that can be used to analyse the integration of renewable energy is presented, and the results in this paper provide the information necessary to identify a suitable energy tool for analysing the integration into various energy-systems under different objectives.
Related Papers (5)
Frequently Asked Questions (18)
Q1. What have the authors contributed in "A sequential linear programming algorithm for economic optimization of hybrid renewable energy systems" ?

In this work an optimization tool for a general HRES is developed: it generates an operating plan over a specified time horizon of the setpoints of each device to meet all electrical and thermal load requirements with possibly minimum operating costs. A case study of a real HRES in Tuscany is presented to test the major functionalities of the developed optimization tool. 

One of the advantages of the proposed architecture is 664 the freedom to add additional devices and tariffs without mod- 665 ifying the existing code. 

282 Released/absorbed power [kWe] and the SOC [%] profile over283 the selected time horizon are the two main outputs of these de-284 vices. 

As the 817CHP does not have a sufficiently large nominal power, the re-818 quired DSM cannot be achieved ∀ i ∈ [49,96] but only during819 the central hours of the day (12:00 - 15:00, i.e. i ∈ [49,61])820 when also PV can generate electrical power. 

The computa- 249 tional efficiency of the developed tool is that, in principle, for 250 typical HRES it can be re-run at each time step similarly to an 251 MPC. 

En- 137 yard et al. [12] use a model predictive controller (MPC) to com- 138 mand the flow of water passing through a storage tank, the wood 139 boiler setpoint temperature to reduce CO2 emissions and oper- 140 ating cost of a boiler system. 

The general for-265 mula for the electrical power production of the k-th generator266 is:267G(k, i) = φ1(k, i)α(k, i) (2)where φ1 formulation depends on the specific generator. 

The SLP algorithm is 564 described in Algorithm 1, in which default parameters are: ε = 565 10−6, ε f = 10−2, ρbad = 0.10 and ρgood = 0.75. 

The general formula for the electrical power consumption of305 the m-th electric load is:306C(m, i) = fL(γ(m, i), i) (6)where fL depends on the load type and γ is the setpoint for the307 time-varying loads. 

The general formula for the electrical power produc-286 tion/absorption of the b-th accumulator is:287A(b, i) = ψ1(b, i)η1(b, i)β (b, i) (4)where ψ1 formulation depends on the battery nominal power288 and η1 is the accumulator power exchange efficiency. 

At each 533 iteration, for a given µ , the authors make an attempt to solve the fol- 534 lowing nonsmooth NLP optimization problem, with bound con- 535 straints only: 536min x Φ(x; µ) (23a)subject to 537xmin ≤ x≤ xmax (23b)The penalty parameter µ is chosen large and increased if nec- 538 essary to promote feasible iterates. 

Software implementation 648The optimizer is implemented in C++ and compiled for both 649 32-bit and 64-bit Windows platforms using Microsoft Visual 650 Studio Express 2012. 

If these constraints do not hold, the behavior of the constraint 600 functions is too nonlinear and the trust region (of the x vari- 601 ables) should be reduced. 

The 397 incentives for generation from renewable sources apply when 398 the HRES is composed by renewable generators of same type, 399 i.e. only PV or WT or biomass burning generators (BM), and 400 electrical loads. 

Any other quantity in each device model is 215 calculated from these setpoints: for instance, in a fuel burning 216 electrical generator, the device input is the ratio between gen- 217 erated power and nominal power, while fuel consumption and 218 generated power are outputs of the device model. 

Considering demand side management, an optimal con- 158 trol method (open loop) is developed to schedule the HRES 159 power flow over 24 h. 

For268 fuel burning generators the correlation for fuel consumption is:269270F(k, i) = G(k, i)LHV (k)ηe(k, i) (3)where LHV is the lower heating value and ηe is the electrical271 efficiency. 

The fuel consumption cost for elec- 392 trical generators, HOT and COLD configurations is expressed 393 as: 394fF(i) = ∑ k∈K cF(k)τF(k, i) (10)where F(k, i) is the fuel rate [kg/h] (at the i-th time step and for 395 the k-th generator) and cF(k) is its unit price [e/kg].