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This paper presents a mixed-integer conic programming model (MICP) and a hybrid solution approach based on classical and heuristic optimization techniques, namely matheuristic,to handle long-term distribution systems expansion planning (DSEP) problems. The model considers conventional planning actions as well as sizing and allocation of dispatchable/renewable distributed generation (DG) and energy storage devices (ESD).The existing uncertainties in the behavior of renewable sources and demands are characterized by grouping the historical data via the ${k}$ -means. Since the resulting stochastic MICPis a convex-based formulation, finding the global solution of the problem using a commercial solver is guaranteed while the computational efficiency in simulating the planning problem of medium- or large-scale systems might not be satisfactory. To tackle this issue, the subproblems of the proposed mathematical model are solved iteratively via a specialized optimization technique based on variable neighborhood descent (VND) algorithm. To show the effectiveness of the proposed model and solution technique, the 24-node distribution system is profoundly analyzed, while the applicability of the model is tested on a 182-node distribution system.The results reveal the essential requirement of developing specialized solution techniques for large-scale systems where classical optimization techniques are no longer an alternative to solve such planning problems.

Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.

Made available in DSpace on 2019-10-06T16:12:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-06-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This paper proposes a multistage convex distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy determines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power distribution network. Electrical Engineering Department São Paulo State University (UNESP), Ilha Solteira Department of Electrical Engineering and Automation Aalto University, Maarintie 8 Electrical Engineering Department São Paulo State University (UNESP), Ilha Solteira FAPESP: 2015/21972-6 CNPq: 305318/2016-0

This paper proposes a solver-friendly model for disjoint, non-smooth, and nonconvex optimal power flow (OPF) problems. The conventional OPF problem is considered as a nonconvex and highly nonlinear problem for which finding a high-quality solution is a big challenge. However, considering practical logic-based constraints, namely multiple-fuel options (MFOs) and prohibited operating zones (POZs), jointly with the non-smooth terms such as valve point effect (VPE) results in even more difficulties in finding a near-optimal solution. In complex problems, the nonlinearity itself is not a big issue in finding the optimal solution, but the nonconvexity does matter and considering MFO, POZ, and VPE increase the degree of nonconvexity exponentially. Another primary concern in practice is related to the limitations of the existing commercial solvers in handling the original logic-based models. These solvers either fail or show intractability in solving the equivalent mixed integer nonlinear programming (MINLP) models. This paper aims at addressing the existing gaps in the literature, mainly handling the MFOs and POZs simultaneously in OPF problems by proposing a solver-friendly MINLP (SF-MINLP) model. In this regard, due to the actions that are done in the pre-solve step of the existing commercial MINLP solvers, the most adaptable model is obtained by melting the primary integer decision variables, associated with the feasible region, into the objective function. For the verification and didactical purposes, the proposed SF-MINLP model is applied to the IEEE 30-bus system under two different loading conditions, namely normal and increased, and details are provided. The model is also tested on the IEEE 118-bus system to reveal its effectiveness and applicability in larger-scale systems. Results show the effectiveness and tractability of the model in finding a high-quality solution with high computational efficiency.

This paper proposes a novel Stackelberg game approach for activating demand response (DR) program in a residential area aiming at addressing the mismatch between the demand and renewable energy generation. In this study, two major players, namely the aggregator as a leader and the consumers as followers, are considered. The aggregator, which owns a wind farm and also receives power from the independent system operator (ISO), strives to obtain the maximum matching between the consumers' demand and forecasted wind power by incentivizing consumers to adjust their load through offering a bonus to them. On the other hand, consumers change their load profiles for obtaining the highest amount of bonuses. Each consumer has two kinds of loads including critical loads, which must be maintained under any circumstances, and the flexible loads, e.g., heating, ventilation, and air conditioning (HVAC) system, which can be regulated for DR purposes. In order to consider the uncertainty associated with the wind generation andthe demands, a scenario-based stochastic programming model has been adopted in this work. Results show the effectiveness of the Stackelberg game model used for interaction between the aggregator and consumers, and the best response that can be served to both of them.

This work aims at proposing a strategy based on an environmental and technical perspective to assess the amount of distributed generation (DG) that can be deployed into an electric distribution network (EDN). The presented approach seeks to minimize the carbon emissions and energy losses while the installed capacity of renewable DG units is maximized. To enable the DG integration without requiring EDN upgrading, the operations of capacitor banks (CBs) and voltage regulators (VR) devices are optimized simultaneously. To analyze the relationship between the installed DG and demand consumption, the effects of voltage-dependent loads are investigated. Several test cases are studied in a 42-node distribution network. Results show that by applying operating actions and determining the hosting capacity of the EDN for deploying DG units, environmental and technical benefits could be achieved.

This paper proposes an indirect load control demand response (DR) strategy for residential houses. A Stackelberg game theory is applied to account for the interaction between the aggregator in one side and the consumers on the other side. The aggregator, which owns a wind power plant, strives to maximize wind power utilization by consumers. Therefore, it motivates the consumers to adjust their load demand according to the forecasted wind power production by offering a bonus to them. On the other hand, consumers attempt to achieve the highest amount of reward by changing their load profile. It is assumed that each consumer has a critical load of which they have no control, and also a flexible load such as heat, ventilation and air conditioning (HVAC) system which can regulate its demand while keeping the temperature in a specified band. In this way, the consumers' comfort level is entirely maintained. In addition, for the sake of considering the uncertainty, several scenarios for wind and also consumers' demand are considered in this work.

This paper develops an optimization model to find the optimal values for the parameters of the double-exponential function. This function can be used to reproduce the volt-time curves of the standard and nonstandard applied impulse voltages in a software environment. Reproducing a similar applied laboratory impulse voltage in a software environment plays a crucial role in obtaining precise results and validates the model to be applied for further studies. In the literature, most of the papers use the existing standard and nonstandard models in which either an RC circuit has been used or a trial and error method has been used to approximately reproduce the applied impulse. However, more often than not, inappropriate adjustments cause a large error in the outcome results. Therefore, the proposed optimization-based approach can act as a facilitating tool for reproducing the nonstandard volt-time curves as close as possible to the laboratory applied impulse. The proposed model is verified by reproducing the volt-time curve of a 125 kV impulse voltage. Comparing the simulated impulse with the experimental impulse voltage shows the usefulness and effectiveness of the proposed approach in adjusting the sensitive parameters of the double-exponential function in EMTP-RV (Electromagnetic Transients Program) software.

Made available in DSpace on 2020-12-10T19:53:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-02-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) There is an increasing concern about controlling and reducing carbon emissions in power systems. In this regard, researchers have focused on managing emissions on the generation side, which is the main source of emissions. Considering emission limits on the generation side result in an increase in locational marginal prices that negatively affects social welfare. However, carbon emissions are a by-product of electricity generation that is used to satisfy the demands on the consumer side. Consequently, demand-side emission control may not be achieved if only the generation is taken into account. In order to fill this existing gap, in this paper, a demand-side management approach aiming at carbon footprint control is proposed. First, the carbon footprint is allocated among the consumers using an improved proportional sharing theorem method. Each consumer learns about their real-time carbon footprint, excess carbon footprint, and the incurred surcharge tax. Then, demands are adjusted via a proper adjustment procedure. This provides enough information for consumers where demand management may result in carbon footprint and demand reductions as well as the exemption of incurred taxes. Profit analysis for both generation and demand sides is carried out to show the effectiveness of the proposed framework using two illustrative case studies. The results obtained, compared with existing policies such as carbon cap, cap-and-trade, and carbon tax, prove the fairness and the advantages of the proposed model for both the demand and the generation sides. Aalto Univ, Dept Elect Engn, Espoo 02150, Finland Univ Castilla La Mancha, ETS Ingenieros Ind, E-13071 Ciudad Real, Spain Sao Paulo State Univ, Dept Elect Engn, BR-15385000 Ilha Solteira, Brazil Sao Paulo State Univ, Dept Elect Engn, BR-15385000 Ilha Solteira, Brazil FAPESP: 2014/22828-3 FAPESP: 2015/21972-6 FAPESP: 2016/14319-7 CNPq: 305318/2016-0

Made available in DSpace on 2018-12-11T17:37:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-08-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) This paper presents a stochastic scenario-based approach to finding an efficient plan for the electrical power distribution systems. In this paper the stochasticity for the distribution system expansion planning (DSEP) problem refers to the loads and wind speed behavior. The proposed DSEP model consist the expansion and/or construction of new substations, installation of new primary feeders and/or reinforcement the existing, installation of wind-distributed generation based, reconfiguration of existing network, and the proposed DSEP is solved considering uncertainty in electric demand and distributed generation. In this regard, a two-stage stochastic programming model is used, wherein the first stage the investment decision is made and the second stage calculates the expected operating value which depends on the stochastic scenarios. The mathematical approach is based on a mixed integer conic programming (MICP) model. By using this MICP model and a commercial optimization solver, finding the optimal global solution is guaranteed. Moreover, in this paper by using the Tabu Search algorithm and take the advantages of a stochastic conic optimal power flow model, an efficient hybrid algorithm is developed. With the aim of comparing the performance of the optimization techniques based on solution of MICP model directly and using a hybrid proposed methodology, they are tested in a 24-node distribution system and the results are compared in detail. Electrical Engineering Department UNESP-São Paulo State University, Av Brasil 056 Department of Electrical Engineering Aalto University School of Electrical Engineering (DEET) Faculty of Enegineering University of Cuenca Electrical Engineering Department UNESP-São Paulo State University, Av Brasil 056

Made available in DSpace on 2018-11-26T17:54:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-08-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) This paper presents an integrated planning framework to optimally determine the location and allocation of renewable-based distributed generation (DG) units, energy storage systems (ESSs), and capacitor banks (CBs). This planning aim at improving the performance of electrical distribution systems (EDSs). In the proposed model, the cost of energy delivered by the substation and the investment costs are minimized. The environmental aspects are taken into account to obtain an efficient environmentally committed plan. The uncertainties due to PV generation and demand profile are considered via external uncertainty indexes in a deterministic environment. The proposed model is a mixed-integer nonlinear programming (MINLP) problem, which is recast to a mixed-integer linear programming (MILP) problem using appropriate linearization techniques. This approximated MILP model is implemented in the mathematical language AMPL, while the commercial solver CPLEX is used to obtain the global optimal solutions. The proposed model is validated by testing on a medium voltage distribution system with 135 nodes under different conditions and topologies. State Univ Sao Paulo, Dept Elect Engn, Power Syst Planning Lab, BR-15385000 Ilha Solteira, SP, Brazil PUC CAMPINAS Pontifical Catholic Univ Campinas, Fac Elect Engn, BR-13086900 Campinas, SP, Brazil State Univ Sao Paulo, Dept Elect Engn, Power Syst Planning Lab, BR-15385000 Ilha Solteira, SP, Brazil FAPESP: 2014/22828-3 FAPESP: 2016/14319-7 CNPq: 305371/2012-6

Made available in DSpace on 2018-12-11T17:32:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-10-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) This paper presents a demand power factor-based approach (DPFA) for finding the maximum loading point (MLP) of a power system using the optimal power flow (OPF). In almost all the presented models in the literature two major drawbacks are obvious: (1) the active and reactive power demands increase equally, constantly, or at the same rate, while in the real world, this hardly ever occurs, and (2) the lack of consideration or misinterpretation of the demand power factor (DPF). This paper addresses the existing drawbacks by proposing a model based on a desired DPF, a threshold predefined by the independent system operator (ISO) that each consumer must maintain to prevent a surcharge. In the proposed DPFA, the active and reactive demands may increase differently resulting in: (1) providing a flexible loading pattern to find the best possible MLP, (2) keeping the desired DPFs at all load buses, and (3) improving the computational efficiency. To verify the DPFA, which is solvable via commercial solvers, several cases such as IEEE 14-, 30-, modified 30-, and 118-bus systems, and a large-scale 2338-bus system are conducted. Results confirm the potential, effectiveness, and superiority of the DPFA compared to the models in the literature. Department of Electrical Engineering Power System Planning Lab Universidade Estadual Paulista (UNESP) E.T.S. de Ingenieros Industriales University of Castilla-La Mancha Department of Electrical Engineering Power System Planning Lab Universidade Estadual Paulista (UNESP) FAPESP: 2014/22828-3 FAPESP: 2016/14319-7

Made available in DSpace on 2020-12-12T02:26:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-09-03 Nowadays, strategies to cope with environmental issues play a crucial role in the development of planning methodologies for electric distribution networks (EDNs). The primary goal of these methodologies is to find the equilibrium point, for which the EDN provides a high-quality service with the most environmentally-committed operation. Based on this fact, an alternative strategic approach for short-term EDN planning is presented. This decision-making scheme is based on classical investment actions to enhance the EDN performance in which the investment and operating costs, as well as the carbon tax surpluses, are minimised simultaneously. Unlike the traditional short-term planning methods and to explore a more accurate approach, the electricity demand is represented by the voltage-dependent exponential load model. By using this representation, substantial benefits related to energy saving can be achieved. To validate the proposed planning scheme, several test cases considering constant power demand and voltage-dependent representation are widely studied on a 135-node distribution network. Additionally, the scalability of the proposed planning scheme is studied by using two medium distribution networks of 42- and 417-node. Numerical results confirm the robustness and applicability of the presented approach as an appropriate way of promoting to an efficient and low carbon tax surplus network. Department of Electrical Engineering São Paulo State University (UNESP) Department of Electrical Engineering and Automation Aalto University, Maarintie 8 Department of Electrical Engineering São Paulo State University (UNESP)

This paper presents an incentive-based demand response (DR) scheme for a small residential area including several houses. It is supposed that there is an aggregator on one side and the residential consumers on the other side, which both of them want to achieve an optimal solution for themselves. For this reason, the Stackelberg game is adopted in this paper to consider the interaction between aggregator and consumers. This game would have one leader (aggregator) and N followers (consumers). Each consumer is assumed to have two kinds of load, namelycritical load, which is not intended for DR, and Heating, ventilation, and air conditioning (HVAC) system, which is considered as a potential for participating in DR. The proposed strategy is organized in a way that aggregator offers incentives to the consumers in order to change their load while consumers try to maximize their bonus getting from aggregator.

This paper proposes a novel energy management framework by combining proactive and reactive approaches to efficiently address the uncertainties associated with generation and demand in islanded and interconnected operation of residential microgrid (MG). The MG considers renewable energy sources (RESs), a diesel generator (DG), and a storage device with the possibility of power exchanges with the grid. The proposed formulation is cast as stochastic mixed-integer linear programming (MILP) model with 24-hour rolling horizon, simulated periodically by updating input data and advancing on an hourly basis for a one year scheduling period. The objective is to minimize the total MG cost while preserving comfort priorities of individual households. The energy balance is potentiated by smart central management system of heating, ventilation and air-conditioning and electric water heater loads in conjunction with exploiting the building thermal inertia of well-insulated buildings. Random outages of MG components are modeled via a two-state Markov chain process, which is considered in the reactive approach. Extensive numerical results are presented to reveal the benefits obtained by coordinating the demand response and supply sources for a practical case of Helsinki, Finland. Finally, the impact of various system parameters on the optimal expected MG cost and risk indices is investigated.

This paper scrutinizes the impacts of demand-side resources (DSRs) on the power system reliability via a novel multi-target generation maintenance (GM) model. The nominated model uses the Lexicographic preferences to hierarchically consider the environmental issues, economics, and reliability of power systems. In this regard, the produced emission, which reflects the per unit produced pollutant values in different locations, is minimized. Taking into account the environmental constraints, the total incurred expenditures, including operating and maintenance costs, reserves costs, and total incentives are also minimized. Subsequently, the GM problem considering the correlation constraints is handled while the reliability index, the average net reserve value (ANRV), is maximized over the scheduling horizon. The DSRs improve the system reliability such that the total costs, and emission level, do not exceed the situation in which DSRs are not available. The GM scheduling is a highly complicated problem and considering DSRs makes it even more complicated. To handle this problem more efficiently, appropriate linearization technique is used, while the proposed model is formulated in GAMS modeling language. To evaluate the capability of DSRs in system reliability improvement, the modified 24-bus IEEE-RTS is conducted. Results indicate that by selecting proper location and incentives, significant improvement is obtained.

The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems.

Made available in DSpace on 2020-12-12T01:52:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-04-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Initiatives such as government programs and investment in low-carbon technologies have been adopted to mitigate the carbon emissions in the electricity sector. These initiatives have resulted in new challenges in the power sector, and to address them adequately, innovative frameworks are required in the electric distribution network (EDN) expansion planning and operation problems. Therefore, this work proposes an environmentally committed asset planning approach to remedy the existing issues to some extent. The proposed strategy investigates the benefits of the simultaneous allocation of several assets such as capacitor banks (CBs), distributed generation (DG) units based on renewable energy sources, and energy storage systems (ESSs). Moreover, an innovative carbon emission trading scheme is formulated in the planning stage to mitigate the CO2 emissions while a demand response program is applied to modify the consumption behavior. The proposed approach is formulated as a two-stage robust mixed-integer programming model, which considers uncertainties associated with the electricity demand and renewable-based DG. To cope with the difficulties of this complex model, utilizing an efficient decomposition algorithm, such as the C&CG decomposition algorithm, is essential. The potential of the proposed approach is studied under different operating conditions and via several test cases on a 137-node EDN. In addition, to validate the performance of the proposed carbon emission scheme, a multi-region 54-node distribution network is adequately evaluated. Results show that by considering simultaneously multiple planning alternatives, carbon emission trading scheme, and the demand response program, the total CO2 emissions are reduced by up to 15%. The Department of Electrical Engineering São Paulo State University (UNESP), Ilha Solteira The Department of Electrical Engineering and Automation Aalto University, Maarintie 8 The Department of Electrical Engineering São Paulo State University (UNESP), Ilha Solteira FAPESP: 2015/21972-6 FAPESP: 2018/12422-0 CNPq: 305318/2016-0

This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.

Made available in DSpace on 2018-12-11T17:24:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-04-19 Short-term planning is a decision-making process that aims at ensuring proper performance of electrical distribution systems (EDSs) within a short period of time. In recent years, this process has faced a significant challenge due to the integration of renewable energy-based technologies. To handle such complicated planning problem most suitably, sophisticated algorithms are required. This paper proposes a mixed-integer linear programming model to find the optimal short-term plan of EDSs considering siting and sizing of capacitor banks and renewable energy sources, conductor replacement of overloaded circuits, and voltage regulators allocation. Besides considering the economic aspects, the environmental issues are also considered to promote a low carbon emission system. To address the uncertainties of electricity consumption and renewable energy output power, a two-stage robust optimization model is used, and to handle this model more efficiently, the column and constraint generation algorithm is applied. A 135-node distribution system is studied under different conditions to assess the performance of the proposed approach. Results show that the planning actions, for each case study, improve the efficiency of EDS and mitigate the pollutant emissions at the distribution level. Departamento de Engenharia Eletrica, Unesp - Ilha Solteira, ILha solteira, Sao Paulo Brazil 15385000 (e-mail: ozzydamedo@gmail.com) Electrical and Computer Engineering, Universidade Estadual Paulista (UNESP), Ilha Solteira, Sao Paulo Brazil 15385000 (e-mail: mahdi.pourakbari@ieee.org) Electrial Engineering, Sao Paulo State University, Ilha Solteira Brazil 15385-000 (e-mail: mant@dee.feis.unesp.br)

Made available in DSpace on 2018-11-26T17:45:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-04-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) There is increasing evidence of the shortage of solver-based models for solving logically-constrained AC optimal power flow problem (LCOPF). Although in the literature the heuristic-based models have been widely used to handle the LCOPF problems with logical terms such as conditional statements, logical-and, logical-or, etc., their requirement of several trials and adjustments plagues finding a trustworthy solution. On the other hand, a well-defined solver-based model is of much interest in practice, due to rapidity and precision in finding an optimal solution. To remedy this shortcoming, in this paper we provide a solver-friendly procedure to recast the logical constraints to solver-based mixed-integer nonlinear programming (MINLP) terms. We specifically investigate the recasting of logical constraints into the terms of the objective function, so it facilitates the pre-solving and probing techniques of commercial solvers and consequently results in a higher computational efficiency. By applying this recast method to the problem, two sub-power- and sub-function-based MINLP models, namely SP-MINLP and SF-MINLP, respectively, are proposed. Results not only show the superiority of the proposed models in finding a better optimal solution, compared to the existing approaches in the literature, but also the effectiveness and computational tractability in solving large-scale power systems under different configurations. State Univ Sao Paulo, Dept Elect Engn, Ilha Solteira, Brazil State Univ Sao Paulo, Dept Elect Engn, Ilha Solteira, Brazil FAPESP: 2014/22828-3 FAPESP: 2016/14319-7 CNPq: 305371/2012-6