Partición de una Red Eléctrica de Distribución Aplicando Algoritmos de Agrupamiento K-means y DBSCAN

En este artículo se propone la metodología para realizar la partición eléctrica de una red de distribución utilizando algoritmos de agrupamiento de datos como K-means y DBSCAN. Los datos se obtienen generando variaciones en los parámetros de la red y simulando el perfil de voltaje con el software OpenDSS. La metodología propuesta se implementa en redes de distribución estándar de prueba IEEE de 34 y 123 barras. Los resultados obtenidos son comparados con métodos obtenidos de la literatura.

An Adaptive Model-Based Real-time Voltage Control Process for Active Distribution Networks Using Battery Energy Storage Systems

The paper presents a centralized real-time adaptive and model-based voltage control algorithm for Active Distribution Networks (ADNs). Differently from the available literature, the proposed algorithm merely relies on the control of Battery Energy Storage Systems (BESSs). In this respect, an experimentally model-fitted two-time constant dynamic model of BESS is used. In particular, this model is used to compute the constraints of the BESSs in terms of DC active power at each controller iteration. These constraints are subsequently used in the central controller for the solution of the optimal voltage control problem. The performances of the proposed method are compared with those obtained for the case where BESSs are modeled as ideal energy reservoirs. Such an assessment is carried out using a numerical example referring to IEEE 13 buses distribution test feeder suitably adapted to include stochastic generation and BESSs

Decentralized voltage control of clustered active distribution network by means of energy storage systems

The paper presents a network partitioning strategy for the optimal voltage control of Active Distribution Networks (ADNs) actuated by means of a limited number of Distributed Energy Storage Systems (DESSs). The proposed partitioning uses a linear programming approach by means of the known concept of voltage sensitivities. Then, two decentralized optimal control algorithms are proposed relying, respectively, on the Thévenin equivalents and a recursive approach. These algorithms are developed using the Multi- Agent System (MAS) concept. With respect to a centralized control algorithm, the aim of the network clustering is to reduce the number of exchanged messages among the clusters when one of the two proposed decentralized control algorithms is adopted. The effectiveness of the two proposed controls is assessed with respect to the performances of the equivalent centralized control using numerical examples composed by the IEEE 13 and IEEE 123 buses distribution test feeders adapted to include stochastic generation and DESSs

Advanced Control of Active Distribution Networks Integrating Dispersed Energy Storage Systems

Due to the increased penetration of Distributed Generations (DGs) in distribution networks, the system control and operation may become quite different from the case of traditional network. Most DGs can only provide intermittent power to the Active Distribution Networks (ADNs) due to the intermittent nature of the resources. Moreover, ADN utilities usually do not own DGs, and have difficulty in controlling directly DGs output powers. The main problem related to the considerable connection of DGs is usually associated to the node voltage quality and line congestion mitigation. Within the above context, the motivating factors for this thesis are supported by the issues related to optimal operation and control of ADNs integrating stochastic and non-stochastic DGs. One of the most promising near-term solution is offered by using distributed Energy Storage Systems (ESSs) which can perform their full role to guarantee a more flexible network. Indeed, the availability of ESSs allows, in principle, to: (i) actively control the power flows into the grid, (ii) indirectly control the voltage profiles along the network feeders and (iii) locally balance the hour/daily and weekly load variations. In this thesis, ESSs are assumed to be the only controllable devices in ADNs. As a result, DGs can be indirectly controlled by means of ESSs. First, this manuscript presents control-oriented model for ESSs. In this respect, the accurate estimation of ESS behavior is utmost important. A generic charge representative model for any ESSs is proposed. Moreover, an improvement of the most common electric equivalent circuit models for the two selected ESSs with different characteristics (namely supercapacitors and batteries) is provided for the development of specific control schemes. They are based on the modeling of redistribution of charges that characterizes the dynamic behaviors of the two devices during long time charging/discharging and relaxation phases. Second, this manuscript presents advanced control/scheduling algorithm for ADNs. The operation and control of ADNs can be achieved either centrally or in a decentralized way. The amount of information to be centrally treated would considerably grow due to the number of generation equipmentâs inserted into the grid and the stochastic operation nature of some of them. This consideration introduces the idea that some ADN operation problems, such as voltage control or line congestion mitigation, can be solved in a distributed manner which would help to relieve the information processing burden and to enhance the system security while preventing unwanted event from propagating through the grid. Therefore, the decentralized schemes are considered subdividing the network into quasi-autonomous areas. To this end, given a set of ESSs optimally located in a balanced and radial ADN, this thesis proposes a network partitioning strategy for the optimal voltage control of ADNs. Thus, the network is decomposed into several areas; each under the control of one ESS which has maximum influence on its corresponding area. Based on this clustering, decentralized scheduling strategies and real-time decentralized control algorithms for the clustered ADNs are proposed. The proposed zonal control capability focuses on voltage control and line congestion management. In both proposed decentralized scheduling and real-time control algorithms the communication among different areas is defined using the concept of Multi-Agent Systems

Оптимізація процесів розподілу енергії в системах з локальними джерелами генерування та акумулювання

Магістерська дисертація присвячена розробці алгоритму, згідно якого,з метою зниження втрат активної потужності буде здійснюватися у реальному часі реконфігурація розподільної мережі, в складі якої є джерела розподіленої генерації. Для прийняття рішень по зміні топології мережі розроблено модель адаптивного прогнозування, яка на кожному кроці прогнозування обирає модель, що показала найкращу точність на попередньому кроці і яка враховує наявної інформації відносно електричного навантаження. Методика вибору оптимальної конфігурації мережі була розроблена на основі класичної задачі вибору місць розмикання, але окрім мети керування режимом в режимі реального часу було враховано наявність в мережі джерел розподіленої генерації. В якості моделей прогнозування було обрано методи машинного навчання (для випадку достатньої інформаційної забезпеченості) і метод нечітких часових рядів (для випадку недостатньої інформаційної забезпеченості). В результаті досліджено було визначено, що застосування алгоритму реконфігурації позитивно впливає на зниження втрат потужності і електричної енергії в мережі і він може використовуватисяоперативним персоналоменергетичних компаній. Очікується, що результати досліджень суттєво сприятимуть подальшому розвитку задачі управління режимами розподільних мереж в реальному часі.The master's thesis is devoted to the development of an algorithm according to which the real-time distribution network will be reconfigured, which includes distributed generation sources in order to reduce active power losses. To make decisions on changing the network topology, an adaptive forecasting model has been developed, which at each step of forecasting selects the model that showed the best accuracy and which takes into account the degree of information security according to the electrical load. The method of selecting the optimal network configuration was developed on the basis of the classical problem of selecting break points, but for the real-time control problem, the availability of distributed generation sources was taken into account. Machine learning methods (for the case of sufficient information security) and the method of fuzzy time series (for the case of insufficient information security) were chosen as forecasting models. As a result, it was investigated that the application of the reconfiguration algorithm has a positive effect on reducing power losses in the network and can be used for control personnel of substations. It is expected that the results of the research will significantly contribute to the further development of the task of managing the modes of distribution networks in real time

Exact Convex Modeling of the Optimal Power Flow for the Operation and Planning of Active Distribution Networks with Energy Storage Systems

The distribution networks are experiencing important changes driven by the massive integration of renewable energy conversion systems. However, the lack of direct controllability of the Distributed Generations (DGs) supplying Active Distribution Networks (ADNs) represents a major obstacle to the increase of the penetration of renewable energy resources characterized by a non-negligible volatility. The successful development of ADNs depends on the combination of i) specific control tools and ii) availability of new technologies and controllable resources. Within this context, this thesis focuses on developing practical and scalable methodologies for the ADN planning and operation with particular reference to the integration of Energy Storage Systems (ESSs) owned, and directly controlled, by the Distribution Network Operators (DNOs). In this respect, an exact convex formulation of Optimal Power Flow (OPF), called AR-OPF, is first proposed for the case of radial power networks. The proposed formulation takes into account the correct model of the lines and the security constraints related to the nodal voltage magnitudes, as well as, the lines ampacity limits. Sufficient conditions are provided to guarantee that the solution of the AR-OPF is feasible and optimal (i.e., the relaxation used is exact). Moreover, by analyzing the exactness conditions, it is revealed that they are mild and hold for real distribution networks. The AR-OPF is further augmented by suitably incorporating radiality constraints in order to develop an optimization model for optimal reconfiguration of ADNs. Then, a two-stage optimization problem for day-ahead resource scheduling in ADNs, accounting for the uncertainties of nodal injections, is proposed. The Adaptive Robust Optimization (ARO) and stochastic optimization techniques are successfully adapted to solve this optimization problem. The solutions of ARO and stochastic optimization reveal that the ARO provides a feasible solution for any realization of the uncertain parameters even if its solution is optimal only for the worst case realization. On the other hand, the stochastic optimization provides a solution taking into account the probability of the considered scenarios. Finally, the problem of optimal resource planning in ADNs is investigated with particular reference to the ESSs. In this respect, the AR-OPF and the proposed ADN reconfiguration model, are employed to develop optimization models for the optimal siting and sizing of ESSs in ADNs. The objective function aims at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedures accounts for (i) network voltage deviations, (ii) feeders/lines congestions, (iii) network losses, (iv) cost of supplying loads (from external grid or local producers) together with the cost of ESS investment/maintenance, (v) load curtailment and (vi) stochasticity of loads and renewables production. The use of decomposition methods for solving the targeted optimization problems with discrete variables and probable large size is investigated. More specifically, Benders decomposition and Alternative Direction Method of Multipliers (ADMM) techniques are successfully applied to the targeted problems. Using real and standard networks, it is shown that the ESSs could possibly prevent load and generation curtailment, reduce the voltage deviations and lines congestions, and do the peak shaving