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The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush–Kuhn–Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs. [ABSTRACT FROM AUTHOR]

New entities and ways of operating electrical power systems have emerged. An example of this is the microgrid (MG), which can be described as a set of power generation units, energy storage systems (ESSs) and demands that can operate as a single set, in order to optimize their energy resources. Moreover, the recent growth in the use of electric vehicles (EVs) in transportation systems has created new challenges to electrical power systems. This article proposes a two-stage stochastic energy scheduling model for an MG to set up the day-ahead optimal decision in the first stage. Real-time operations, depending on wind and photovoltaic solar power, baseload demand and EV demand variability, are taken into account in the second stage. The proposed model considers conventional generators and alternating current (AC) linearized power flow constraints. Besides, a detailed ESS operation is considered, including degradation costs, and nonlinear charging/discharging as well as efficiency variation dependent of state of charge (SOC), for lead-acid and lithium-ion technologies. EV demand behavior is generated by probability-based algorithms that use the Monte Carlo method to perform their simulations, which are based on data of initial SOC, plugin/out times, type of EVs, and chargers. Finally, the proposed model is successfully validated with an IEEE-37 bus system. The results support the superiority of the proposed model over existing works, showing significant variations in battery bank (BB) operation. The computational time is similar to previous works, despite adding new constraints that increase the complexity. [ABSTRACT FROM AUTHOR]

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability. [ABSTRACT FROM AUTHOR]

The increased deployment of distributed generation calls for the coordination and interaction between the transmission and distribution levels. This requirement is particularly relevant for planning purposes when renewable-based generation is involved. Unfortunately, in current industry practice, transmission and distribution network planners solve their problems independent of each other, thereby leading to suboptimal solutions. Within this context, this paper addresses the integrated expansion planning problem of transmission and distribution systems where investments in network and generation assets are jointly considered. Several alternatives are available for the installation of lines as well as conventional and renewable-based generators at both system levels. Thus, the optimal expansion plan identifies the best alternative for the candidate assets under the uncertainty associated with demand and renewable-based power production. The proposed model is an instance of stochastic programming wherein uncertainty is characterized through a set of scenarios that explicitly capture the correlation between the uncertain parameters. The resulting stochastic program is driven by the minimization of the expected total cost, which comprises the costs related to investment decisions and system operation. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program for which finite convergence to optimality is guaranteed. Numerical results show the effective performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Continuity of supply plays a significant role in modern distribution system planning and operational studies. Accordingly, various techniques have been developed for reliability assessment of distribution networks. However, owing to the complexities and restrictions of these methods, many researchers have resorted to several heuristic optimization algorithms for solving reliability-constrained optimization problems. Therefore, solution quality and convergence to global optimality cannot be guaranteed. Aiming to address this issue, two salient mathematical models are introduced in this paper for topology-variable-based reliability evaluation of both radial and radially-operated meshed distribution networks. Cast as a set of linear expressions, the first model is suitable for radial networks. The second model relies on mixed-integer linear programming and allows handling not only radial networks but also radially-operated meshed distribution grids. Therefore, the proposed formulations can be readily incorporated into various mathematical programming models for distribution system planning and operation. Numerical results from several case studies back the scalability of the developed models, which is promising for their further application in distribution system optimization studies. Moreover, the benefits of the proposed formulations in terms of solution quality are empirically evidenced. [ABSTRACT FROM AUTHOR]

A microwave device able to detect differential-mode to common-mode conversion (or vice versa) in a four-port balanced circuit is proposed. For mode conversion detection, the balanced circuit should be fed by a differential-mode signal, generated by means of a rat-race balun from a single-ended signal. By connecting the pair of output ports of the balanced circuit under test to one of the two pairs of isolated ports of a second rat-race coupler, conversion to the common-mode (if it exists) can be detected and recorded in the $\Sigma $ -port. Since the signal level at the output (single-ended) port depends on mode conversion efficiency, the complete two-port structure, including the pair of rat-race couplers plus the balanced circuit in between, can be used as a comparator or differential microwave sensor based on mode conversion. In such a sensor, the working principle is symmetry disruption (caused, e.g., by an asymmetric dielectric load), and the output variable is the transmission coefficient, related to the level of asymmetry. A detailed analysis, considering an arbitrary unbalanced four-port test structure (to account for symmetry disruption), is carried out. Then, such analysis is particularized to the case of a pair of unbalanced and uncoupled matched lines, and the conditions for sensitivity optimization are obtained. Finally, a differential sensor and comparator based on a pair of balanced (and meandered) matched lines, designed according to the guidelines for sensitivity optimization, is fabricated and validated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a trilateral bilevel stochastic mixed integer bilevel linear programming (MIBLP) model to handle a joint master-slave operation-planning problem. This model considers the interactions among an integrated community energy system (ICES), prosumers, and the wholesale electricity market (WM). The ICES, in the upper level, makes a joint optimal photovoltaic (PV) and energy storage system plan taking into account the concurrent interactions with the WM and prosumers to maximize its profit. On the other hand, in the lower level, the prosumers aim at minimizing their bills via an optimal PV-package sizing while interacting with the ICES and the WM simultaneously. The strong duality theorem is used to recast this MIBLP model into an equivalent single-level model. Since the lower level is also an operation-planning problem, this recast results in a mixed integer nonlinear programming (MINLP) model that prevents finding a satisfactory solution. To remedy this issue, sensitivity analysis and a separation-based linearization technique are applied. Numerical results illustrate the effective performance of the proposed business model while providing valuable information for the ICES and consumers. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage stochastic bilevel model for energy management of an active distribution network with multi-microgrids. The distribution network operator connected to the upstream high voltage grid and interacting with multi-microgrids has the task of scheduling the whole system to feed loads, providing safe operation and dealing with uncertainty due to changes in the loads and renewable generation. The multi-microgrids connected to the nodes of the distribution system aim to schedule their productions to feed their internal loads and exchange power with the distribution network operator in both day-ahead and real-time frames. With this purpose, a bilevel model is presented for modeling the interaction between the distribution network operator and the multi-microgrids in which the distribution network operator is located at the upper level and all multi-microgrids at the lower level. The proposed bilevel model is formulated as a mathematical program with equilibrium constraints that is transformed into a mixed-integer linear model through Karush–Kuhn–Tucker conditions and linearization rules. A case study illustrates the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Chipless RF identification (RFID) technology has emerged as an alternative to RFID systems that have tags equipped with silicon integrated circuits (ICs), or chips [1]-[12]. The main advantage of chipless RFID systems over their chipped counterparts is the lower cost of the tags, which results from the fact that the ID code is contained in a printed planar encoder (implementable with conductive inks) rather than stored in a silicon chip. Low-cost tags (below the US$0.01 barrier) are necessary in many RFID applications involving items that have a moderate or low price and where chip-based tagging would represent a significant penalty in terms of the overall expense. [ABSTRACT FROM AUTHOR]

An influential factor in enhancing the attendance services, mainly in commercial and emergency sectors, is the vehicular technology used to transport people, goods, or equipment. Although hybrid electric vehicles (HEVs) represent a sustainable transport alternative, the existing technical limitations such as battery and fuel capacities, and autonomy, among others, highlight the provision of an efficient automation tool. The tool can serve to enhance the operational performance of the HEV by selecting the proper driving mode (on fuel or electricity), and the navigation strategies to the delivery and charging points in urban areas. This paper proposes a two-stage methodology that allows the HEVs operators to automate the operational performance of a heterogeneous HEV fleet on a city map. Each stage is handled by its corresponding optimization model. In the first stage, the total navigation time and the battery lifetime of the fleet during the operation are optimized. In this stage, constraints related to charge-sustaining/charge-depleting modes, state of charge (SoC) of the HEVs battery, and deliveries schedules are taken into account. To this end, operating strategies related to the performance of different types of existing HEV technologies are anonymously considered. In the second stage, the best operating strategy among all the operating strategies is selected while considering the capacity of HEVs to deliver a given quantity of goods. Moreover, uncertainties during the HEV navigation are simulated considering the change in traffic density of the urban roads as a function of the levels of service (LOS). Results show that the proposed methodology establishes an efficient operational scheme for a HEVs fleet, ensuring a significant reduction of energy usage as well as mitigating the CO2 emissions. [ABSTRACT FROM AUTHOR]

This paper addresses the multistage or dynamic investment decision-making problem arising in a distribution system within a transactive energy environment. Under such a framework, three agents are involved, namely the distribution company (DISCO), the owner of distributed generation (DG) units (DGENCO), and the independent distribution system operator (IDSO). Here, we propose characterizing this planning problem as an instance of trilevel programming. In the upper level, the DISCO identifies the optimal investment plan in network assets and the best potential locations for DG in order to supply the customer demand at maximum profit. In the middle level, the DGENCO determines the best location, sizing, and timing for DG installation so that the corresponding profit is maximized. Finally, in the lower level, the IDSO is responsible for the optimal operation of the expanded distribution system. The resulting mixed-integer trilevel program is solved by a novel approach relying on Benders decomposition. The performance of the proposed approach has been illustrated with a case study based on a 37-node test system. [ABSTRACT FROM AUTHOR]

The calculation of standard reliability indices is critical for assessing quality service and guiding the planning and operation of distribution systems. Traditionally, such calculation has relied on the application of analytical methods based on simulation techniques, which preclude the use of exact methodologies when reliability assessment is incorporated into operation and planning models. This shortcoming has been recently addressed by the development of two optimization-based approaches for the analytical reliability assessment of distribution systems. Unfortunately, both methods are either incomplete or computationally expensive as compared with those based on simulation. In order to overcome these issues, this paper presents a novel and efficient algebraic approach to calculate the standard network-dependent reliability indices of distribution systems. As a distinctive feature over previous non-simulation-based methods, no optimization process is involved. Thus, the proposed approach relies on the solution of a set of linear equations for which effective algorithms are available. Several benchmarks including a real-life 1080-node system have been used to demonstrate the computational superiority of the proposed method. The successful numerical experience supports the suitability of the proposed algebraic model for reliability-constrained distribution system operation and planning. [ABSTRACT FROM AUTHOR]