• Energy Research

In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.

Publisher Copyright: © 1969-2012 IEEE. Optimal placement of switches and tie lines is an integral task in distribution system planning. Owing to the problem complexity and the presence of binary decision variables, using heuristic methods for obtaining a close to optimal switch and tie line plan is a common practice in industry. Efforts to employ mixed-integer linear programming (MILP) to guarantee the solution optimality also tend to sacrifice the modeling accuracy through oversimplifying assumptions to make the problem tractable. Aiming to tackle these challenges, we present an efficient, yet accurate, MILP model for optimal switch and tie line planning. The proposed model avoids common simplifying assumptions in the state-of-the-art MILP models while preserving the solving efficiency. In order to demonstrate the applicability and scalability of the proposed MILP approach, it is applied to multiple test networks, and the results are compared with those of a fast heuristic model. The outcomes not only represent the high efficiency and accuracy of the MILP model but also validate the close to optimality of the heuristic approach developed for practical applications as a module in a commercial distribution system planning toolbox. Peer reviewed

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.

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.

This paper presents a hierarchically distributed algorithm for the execution of distribution state estimation function in active networks equipped with some phasor measurement units. The proposed algorithm employs voltage-based state estimation in rectangular form and is well-designed for large-scale active distribution networks. For this purpose, as the first step, the distribution network is supposed to be divided into some overlapped zones and local state estimations are executed in parallel for extracting operating states of these zones. Then, using coordinators in the feeders and the substation, the estimated local voltage profiles of all zones are coordinated with the local state estimation results of their neighboring zones. In this regard, each coordinator runs a state estimation process for the border buses (overlapped buses and buses with tie-lines) of its zones and based on the results for voltage phasor of border buses, the local voltage profiles in non-border buses of its zones are modified. The performance of the proposed algorithm is tested with an active distribution network, considering different combinations of operating conditions, network topologies, network decompositions, and measurement scenarios, and the results are presented and discussed.

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. La confiabilidad es un factor esencial en la planificación de la expansión de la red de distribución. Sin embargo, las técnicas estándar de evaluación de la confiabilidad de la distribución se basan en la cuantificación del impacto de un conjunto de eventos preespecificados en la continuidad del servicio a través de la simulación de cortes de componentes, uno a la vez. Debido a esta naturaleza basada en la simulación, la incorporación de la confiabilidad en la planificación de la expansión de la red de distribución ha requerido habitualmente la aplicación de enfoques heurísticos y metaheurísticos. Recientemente, se han propuesto modelos alternativos de programación lineal entera mixta (MILP) para la planificación de la expansión de la red de distribución teniendo en cuenta la confiabilidad. No obstante, tales modelos sufren de una baja eficiencia computacional o de una simplificación excesiva. Para superar estas deficiencias, Este documento propone un modelo MILP mejorado para la planificación de la expansión de la red de distribución con restricciones de confiabilidad en varias etapas. Aprovechar un modelo de evaluación de confiabilidad eficiente pero preciso, proponer una técnica personalizada para imponer de manera efectiva la operación radial, así como utilizar medidas pragmáticas para modelar los costos relacionados con la confiabilidad, son las características más destacadas de este trabajo. En este sentido, los costos prácticos relacionados con la confiabilidad se consideran en función de los esquemas de incentivos de confiabilidad y la pérdida de ingresos debido a la energía no entregada durante los apagones de los clientes. El enfoque de planificación propuesto se prueba en cuatro redes con 24, 54, 86 y 138 nodos para ilustrar su eficiencia y aplicabilidad. Las características más destacadas de este trabajo son proponer una técnica personalizada para imponer con eficacia la operación radial, así como utilizar medidas pragmáticas para modelar los costos relacionados con la confiabilidad. En este sentido, los costos prácticos relacionados con la confiabilidad se consideran en función de los esquemas de incentivos de confiabilidad y la pérdida de ingresos debido a la energía no entregada durante los apagones de los clientes. El enfoque de planificación propuesto se prueba en cuatro redes con 24, 54, 86 y 138 nodos para ilustrar su eficiencia y aplicabilidad. Las características más destacadas de este trabajo son proponer una técnica personalizada para imponer con eficacia la operación radial, así como utilizar medidas pragmáticas para modelar los costos relacionados con la confiabilidad. En este sentido, los costos prácticos relacionados con la confiabilidad se consideran en función de los esquemas de incentivos de confiabilidad y la pérdida de ingresos debido a la energía no entregada durante los apagones de los clientes. El enfoque de planificación propuesto se prueba en cuatro redes con 24, 54, 86 y 138 nodos para ilustrar su eficiencia y aplicabilidad.

The increasing penetration of Photovoltaic (PV) generation results in challenges regarding network operation, management and planning. Correspondingly, Distribution Network Operators (DNOs) are in the need of totally new understanding. The establishment of comprehensive standards for maximum PV integration into the network, without adversely impacting the normal operating conditions, is also needed. This review article provides an extensive review of the Hosting Capacity (HC) definitions based on different references and estimated HC with actual figures in different geographical areas and network conditions. Moreover, a comprehensive review of limiting factors and improvement methods for HC is presented along with voltage rise limits of different countries under PV integration. Peak load is the major reference used for HC definition and the prime limiting constraint for PV HC is the voltage violations. However, the varying definitions in different references lead to the conclusion that, neither the reference values nor the limiting factors are unique values and HC can alter depending on the reference, network conditions, topology, location, and PV deployment scenario.