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This paper addresses the problem of economic/environmental optimal power flow with a multiobjective formulation using a second-order conic programming (SOCP) optimization model. This problem formulation considers renewable energy sources (RES), fossil-fuel-based power generation units, and voltage control. The proposed SOCP model is a stochastic scenario-based approach to deal with RES and load behavior uncertainties. An ε-constrained algorithm is used to handle the following three objective functions: (1) the costs of power generation, (2) active power losses in the branches, and (3) the emission of pollutant gases produced by fossil-fuel-based power generation units. For comparative purposes, the SOCP model is also presented using a linearized formulation, and numerical results are presented using a 118-bus system. The results confirm that changing the energy matrices directly affects the cost of objective functions. Additionally, using a linearized SOCP model significantly reduces reactive power violation in the generation units when compared to the nonlinearized SOCP model, but also increases the computational time consumed.

Publisher Copyright: © 2022 Elsevier Ltd In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the second NESC (large-scale) that considers 14 consumer communities is analyzed for the most complete case study (Case 2). Within each NESC, consumer communities are differentiated by the household income and the types of SBs (individual and shared) that support each community. The results corroborate the applicability of the MILP model to real case studies on a diverse scale, guaranteeing the efficient use of PG at the same time that each SB seeks the most optimized operation.

Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Nos. 52171292, 51939001), Dalian Outstanding Young Talents Project (Grant No. 2022RJ05). Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents an optimal active power dispatch (OAPD) problem that, unlike common economic dispatch problems, precludes unwanted mismatches on realistic power systems. The OAPD is formulated by considering the unified power flow controller (UPFC), a versatile device from the flexible AC transmission systems. However, the resultant turns into a highly nonlinear and complex optimization problem, which requires a powerful evolutionary algorithm to determine the optimal solutions. Toward this end, this paper explores the use of comprehensive learning particle swarm optimization and differential evolution as a hybrid configuration in a fuzzy framework, called hybrid fuzzy-based improved comprehensive learning particle swarm optimization-differential evolution, to address the proposed problem. To demonstrate the performance of the proposed algorithm, a set of benchmark problems, including real-world constrained optimization problems as well as a profound analysis of Schwefel problem 2.26 are provided. Moreover, to authenticate its effectiveness in solving power and energy-related problems with quite a few decision variables, four different power systems, 3-unit, 6-unit IEEE 30-bus, 10-unit, and 40-unit systems, are implemented. The IEEE 30-bus system is opted for profoundly analyzing the performance of the proposed algorithm in handling the optimal power dispatch problem considering security constraints and UPFC device, where an enhancement, at least $74,000 saving in a 365-day horizon, in total generation cost is obtained. Simulation results also validate that evolutionary algorithms need to be improved/hybridized to achieve better equilibrium between exploration and exploitation processes in a timely manner while solving power and energy-related problems.

Funding Information: Arsalan Najafi would like to acknowledge the support by Polish National Agency for Academic Exchange for the grant No. PPN/ULM/2020/1/00196 . Publisher Copyright: © 2022 The Author(s) This paper proposes a novel bi-level strategic energy trading framework to minimize the operation cost of the distribution network (DN) interacting with peer-to-peer (P2P) transactive energy hubs with electric vehicles. A distribution system operator at the upper level minimizes its total cost from purchasing electricity in the wholesale market, generating with its own microturbines, and selling electricity to the energy hubs. Each transactive energy hub at the lower level reacts to the offer price received from the upper level, interacting with the other energy hubs. Each energy hub has a parking lot to harvest the benefit from asynchronous storage of electricity in other energy hubs stemming from the difference between the arrival or departure times of the electric vehicles. A single-leader multi-follower game approach is developed to model the DN-energy hubs game structure. Then, an iterative model is proposed to find the equilibrium point between the leader and the followers, while the distributed problemof the interaction between the followers at the LL is solved by the Alternating Direction Method of Multipliers (ADMM). Numerical results for the IEEE 33-bus test system with two energy hubs show the effectiveness of the proposed transactive model between the energy hubs and the DN.

Funding Information: This research was supported by Business Finland grant 7439/31/2018 . Publisher Copyright: © 2022 The Authors A Virtual power plant is defined as an information and communications technology system with the following primary functionalities: enhancing renewable power generation, aggregating Distributed Energy Resources and monetizing them considering the relevant energy contracts or markets. A virtual power plant also includes secondary functionalities such as forecasting load, market prices and renewable generation, as well as asset management related to the distributed energy ressources. Home energy management systems and building energy management systems have significant overlap with virtual power plants, but these bodies of research are largely separate. Machine learning has recently been applied to realize various functionalities of these systems. This article presents a 3-tier taxonomy of such functionalities. The top tier categories are optimization, forecasting and classification. A scientometric research methodology is used, so that a custom database has been developed to capture metadata from all of the articles that have been included in the taxonomy. Custom algorithms have been developed to generate infographics from the database, to visualize the taxonomy and trends in the research. The paper concludes with a discussion of topics expected to receive a high number of publications in the future, as well as currently unresolved challenges.

Funding Information: Arsalan Najafi would like to acknowledge the support by Polish National Agency for Academic Exchange for the grant No. PPN/ULM/2020/1/00196 . Publisher Copyright: © 2021 The Author(s) Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6 % taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model.

Funding Information: Lucas do Carmo Yamaguti, Juan M. Home-Ortiz, and José Roberto Sanches Mantovani acknowledge the support of the São Paulo Research Foundation (FAPESP) under grants 2019/01841-5 2015/21972-6, the support of the Coordination for the Improvement of Higher Education Personnel (CAPES) finance code 001, and the support of the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 304726/2020-6. Publisher Copyright: © 2022 IEEE. The Optimal Power Flow (OPF) problem is commonly formulated considering one central operator manager (centralized approach). However, in practice, the power system is structured by interconnected areas, controlled by several system operators, which are dependent on their neighbors and must exchange sensitive data with each other. In addition, some generation units must be restricted in some zones of operation to avoid negative operational effects. This paper proposes a mixed-integer nonlinear programming model to solve the decentralized AC-OPF considering prohibited operation zones (POZ). A matheuristic algorithm based on the variable neighborhood descent heuristic method is used to deal with the integer variables of the problem, while a non-linear optimization solver is used to solve the optimal power flow with continuous variables. The proposed model and solution technique are validated using the IEEE 118-bus system, ensuring that the decentralized model determines solutions close to the centralized model without and with POZ constraints.

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.

Optimal power flow (OPF) is one of the most important tools in power system operation and control, which determines the minimum operating cost and retains the control variables in their secure boundaries. This paper takes into account several unbridled practical constraints in the OPF problem, three of which – that is – valve-point effect, multi-fuel option, and, above all, prohibited operating zone are the most conspicuous ones. Further, the flexible alternating current transmission systems (FACTS) devices are considered, as well, which have several merits such as decreasing the active power transmission loss, controlling the power flow, and improving the voltage stability/profile, to name but a few. Accordingly, thyristor controlled series capacitor (TCSC) – the most popular and common component of the FACTS equipment’s category – is utilized in this study. As a result, the OPF problem integrated with such practical constraints referred to above as well as FACTS devices becomes a highly nonlinear-nonconvex optimization problem and to solve it, a reliable and efficient evolutionary algorithm such as fuzzy-based improved comprehensive-learning particle swarm optimization (FBICLPSO) algorithm is introduced. The proposed approach is scrutinized on IEEE 30-bus test system, which is a commonly used test system for solving the non-smooth and non-convex versions of the OPF problem. Comparing the obtained results by the proposed algorithm with the available alternatives in the literature corroborate the potential and effectiveness of the proposed approach.

In this paper, a mixed-integer linear programming model is proposed to optimize hybrid electric vehicle (HEV) navigation modes on the city map, namely the problem of the optimal selection of navigation modes (OSNMs). The OSNMs problem of the HEV as part of the operating strategy is obtained considering a constraint set related to CO 2 emissions reduction, efficient battery charging, and the optimal scheduling of deliveries. Uncertainties in the HEV navigation on urban roads are modeled using probability values assigned to an established set of traffic density values according to the levels of service. The model is implemented in a mathematical programming language (AMPL) and solved using the commercial solver CPLEX. The case study considers real data related to the Prius Prime technology and shows the effectiveness of automating the HEV navigation modes considering CO 2 emissions reduction levels during an operating strategy.

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.

Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits.

To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments as well as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, the need for storage is also increasing to maintain a continuous power supply. Hence, this paper presents a MILP-based model to optimize the size of thermal storage required to satisfy the annual DH demand of a community solely by P2H conversion employing renewable energy. The DH is supplied by the optimal operation of a novel 2-km deep well heat pump system (DWHP) equipped with thermal storage. To avoid computational intractability, representative time steps with varying time duration are chosen by employing hierarchical agglomerative clustering that aggregates adjacent hours chronologically. The value of demand response and the effect of interannual weather variability are also analyzed. Numerical results from a Finnish case study show that P2H conversion utilizing small thermal storage in tandem with the DWHP is able to cover the annual DH demand, thus leading to a carbon-neutral DH system and, at the same time, mitigating the curtailment of excessive wind generation. Compared with the annual DH demand, an average thermal storage size of 29.17 MWh (2.58%) and 13.99 MWh (1.24%) are required in the business-as-usual and the demand response cases, respectively.

Utilizing multi-carrier energies such as wind energy, electric vehicle (EV) and battery banks are a significant step toward a cleaner production. Hence, This paper proposes a stochastic-based decision-making framework for the efficient short-term management of smart energy hub (EH) in restructured power systems with high penetration of renewable energy. The electrical and natural gas carriers are the input of smart EH, while the electricity and heat demands are considered as the outputs. The EH, including battery storage system (BSS) and EV fleet, is managed in the regulation market and day-ahead (DA) horizons. The energy hub operator makes optimal decisions regarding the natural gas network and energy supply for the thermal and electricity customers. An optimal self-scheduling model is developed to take into account the day ahead (DA) and regulation markets (RM) and decisions regarding generations of electrical and thermal devices as well as EV aggregator decisions. The primary goal of the proposed frameworkis minimizing the cost of procuring electricity and heat energy carriers in DA and regulation markets, including upward/downward regulations via a stochastic mixed-integer linear programming (MILP) approach. In order to get the uncertainty around the exact outcomes of RM prices, wind generation, and EV patterns, the model also takes into account the conditional value at risk (CVaR) term. The proposed formulation is examined by applying to a smart EH. Results show the effectiveness and usefulness of the proposed framework in managing the smart EHs efficiently. (c) 2020 Elsevier Ltd. All rights reserved.

Cloud computing is a megatrend in industry digitalization. The virtual power plant (VPP) is especially suited for cloud deployment since it can manage energy resources that are not co-located. However, there is a lack of research in VPP cloudification, mainly related to multi-tenancy, a key technology behind the efficiency benefits of cloud computing. Cloudification is complicated as power systems and distributed energy resources frequently employ interfaces and protocols from an earlier era. In particular, IEC 60870-5-104 is a well-established standard for telecontrol in electrical engineering and power system automation applications. This paper proposes a new perspective on VPP interoperability via the application of cloud computing. IEC 60870-5-104 enabled systems are integrated into third-party systems in the cloud. Examples of such third-party systems are the Internet of Things-enabled distributed energy resources or electricity market information systems. This paper proposes a new problem definition of VPP-connected systems through establishing the said third party systems as tenants in a multi-tenant architecture. To define a general industry standards-based system architecture, both the software design methods and cloud computing architectural concepts are applied. An implementation is presented that is ready to integrate into systems in the field.

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.

Preventing the medium voltage (MV) transformer fault by protecting transformers against indirect lightning strikes plays a crucial role in enhancing the continuous service to electricity consumers. Surge arresters, if selected properly, are efficient devices in providing adequate protection for MV transformers against transient overvoltage impulses while preventing unwanted service interruptions. However, compared to other protective devices such as the spark gap, their prices are relatively high. The higher the surge arrester rating and energy absorption capacity are, the higher the prices go. This paper proposes an inductor-based filter to limit the energy pushed into the surge arrester, and consequently to prevent any unwanted failure. An energy-controlled switch is proposed to simulate the fault of the surge arrester. Surge arresters with different ratings, e.g., 12 kV, 18 kV, 24 kV, 30 kV, 36 kV, and 42 kV with two different classes of energy, namely, type a and type b, are tested under different indirect lightning impulses such as 100 kV, 125 kV, 150 kV, 175 kV, 200 kV, 250 kV, 300 kV, and 500 kV. Furthermore, these surge arresters are equipped with different filter sizes of 100 μH, 250 μH, 500 μH, and 1 mH. Results prove that equipping a surge arrester with a proper filter size enhances the performance of the surge arrester significantly such that a high rating and somewhat expensive surge arrester can be replaced by a low rating and cheap surge arrester while providing similar or even better protective performance for MV transformers. Therefore, such configurations not only enhance the protective capability of surge arrester, but also reduce the planning and operating costs of MV networks.

Direct lightning strikes on overhead phase conductors result in high overvoltage stress on the medium voltage (MV) terminals of pole-mounted transformers, which may cause considerable damage. Therefore, introducing an efficient protection strategy would be a remedy for alleviating such undesirable damages. This paper investigates the optimized protection of MV transformers against direct lightning strikes on the phase conductors. To this end, first, the impacts of grounding densities (number of grounded intermediate poles between every two successive transformer poles) on the probability of overvoltage stress on transformer terminals are investigated. Then, the implications of guy wire, as a supporting device for ungrounded intermediate poles, on reducing the overvoltage stress on transformers, are studied. Finally, the role of a surge arrester in mitigating the overvoltage stress of non-surge-arrester-protected transformer poles is scrutinized. The investigations are conducted on a sample MV network with 82 wood poles comprising 17 pole-mounted transformers protected by spark gaps. To provide in-depth analysis, two different poles, namely creosote- and arsenic-impregnated poles, are considered under wet and dry weather conditions. A sensitivity analysis is performed on grounding distances and on a combination of guy wire and grounded intermediate poles while taking into account soil ionization. The results provide a clear picture for the system operator in deciding how many grounded intermediate poles might be required for a system to reach the desired probabilities of transformers experiencing overvoltage stress and how the surge arrester and guy wires contribute to mitigating undesirable overvoltage stress.

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

This paper tackles the challenges of offshore wind farm owners participating in the electricity market, aiming at maximizing their profit. Decreasing the subsidies, which used to support the wind farm owners in increasing the penetration of wind-based generation, resulted in new challenges. The owners need to compete not only with each other but also with the existing conventional producers in the electricity market. In this work, in order to have a more practical model, the day-ahead and balancing markets are considered. Moreover, the impact of energy storage devices on the profit of wind farm owners is also investigated. The stochastic programming approach is used to handle the existing uncertainties in the output power of the wind farm, and the day-ahead and balancing market prices. Four different case studies are conducted to analyze the potential of the proposed model. Results show the effectiveness of considering the day-ahead and balancing markets jointly by increasing the profit of windfarms owners. Moreover, the storage device highly increases the degree of freedom in participating in different electricity markets.

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 a model for multiperiod generation and transmission expansion planning (G&TEP) problem in the presence of uncertainties in the strategies of market participants. The effects of demand response (DR) and fixed series compensation (FSC) devices allocation are considered for peak shaving purposes and optimal utilization of transmission capacity, respectively. This may cutback the generating expansion capacity and transmission investment costs. The optimal expansion plan is achieved while the uncertainties in the generators' offers and demands' bids are considered in the market model. In this model, the DR preferences are integrated into the market clearing process of the independent system operator (ISO), which is applied to the load aggregators according to the locational marginal and market clearing prices. Shifting the demand, curtailing the peak, and onsite generation are considered as load reduction strategies in the demand response program. The ISO optimizes the decision submitted by generating companies and load aggregators in the presence of uncertainties. The proposed model is applied to the Garver, single-, two-, and four-area IEEE-RTS 24-bus systems to show the effectiveness of the multioptional DR program and the FSC devices in the dynamic G&TEP problems.

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.

This paper presents a mixed integer linear programming (MILP) model to optimizing the maintenance costs during the operation of the hybrid electric vehicles (HEVs) aiming at reducing CO 2 emissions during operating along of the day. This target is obtained by an appropriate selection of navigation modes and the optimal scheduling of deliveries. The proposed model considers the average speed, battery state of charge (SOC), and the set of deliveries to be made by each type of HEV. A set of constraints that ensures the performance of the HEVs is considers while the uncertainties of the trip on each urban road are modeled using the traffic density values according to the levels of service (LOS). The proposed model is implemented in AMPL and solved using the solver CPLEX showed the effectiveness in the evaluation of each type of HEV technology.

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

Made available in DSpace on 2018-11-26T16:27:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-02-04 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) This study presents a tie line planning and its effects on the operating cost and environmental issues of power systems via a novel multi-area active-reactive optimal power flow (MA-AROPF) model. In this study, the authors focus on the significant role of tie line planning on power system operation. To select an appropriate tie line, a modified sensitivity index is used, which not only reduces the operating cost and emissions, but also enhances the voltage stability of individual areas and the entire power system. These benefits are obtained by increasing the degree of freedom of the power system through providing uniform economic and emission dispatch. Moreover, in this study, to address the drawbacks of commonly used decomposition methods for solving MA-AROPF, an integrated model is proposed. An AROPF that considers the environmental effects is a highly non-linear problem, and the multi-area consideration of such problems via tie line planning makes it an even more complicated and exceedingly non-linear problem. For didactic purposes and to verify the model, a small two-area system is considered in detail, while to show the effectiveness of the proposed approach, a three-area system consisting of 14-, 30-, and 118-bus IEEE test systems is conducted. State Univ Sao Paulo, Dept Elect Engn, Ilha Solteira, Brazil Univ Estadual Campinas, Dept Syst & Energy, Campinas, SP, Brazil State Univ Sao Paulo, Dept Elect Engn, Ilha Solteira, Brazil FAPESP: 2014/22828-3 FAPESP: 2013/23590-8 CNPq: 305371/2012-6