BATTPOWER Toolbox Memory-Efficient and High-Performance MultiPeriod AC Optimal Power Flow Solver
ABSTARCT :
With the introduction of massive renewable energy sources and storage devices, the traditional process of grid operation must be improved in order to be safe, reliable, fast responsive and cost efficient, and in this regard power flow solvers are indispensable. In this paper, we introduce an Interior Point-based (IP) Multi-Period AC Optimal Power Flow (MPOPF) solver for the integration of Stationary Energy Storage Systems (SESS) and Electric Vehicles (EV). The primary methodology is based on: 1) analytic and exact calculation of partial differential equations of the Lagrangian sub-problem, and 2) exploiting the sparse structure and pattern of the coefficient matrix of Newton-Raphson approach in the IP algorithm.Extensive results of the application of proposed methods on several benchmark test systems are presented and elaborated, where the advantages and disadvantages of different existing algorithms for the solution of MPOPF, from the standpoint of computational efficiency, are brought forward. We compare the computational performance of the proposed SchurComplement algorithm with a direct sparse LU solver. The comparison is performed for two different applicational purposes: SESS and EV. The results suggest the substantial computational performance of Schur-Complement algorithm in comparison with that of a direct LU solver when the number of storage devices and optimisation horizon increase for both cases of SESS and EV. Also, some situations where computational performance is inferior are discussed.
EXISTING SYSTEM :
? The existing tools lack some key features that are used by power systems utilities to carry out steady state analysis, e.g. distributed slack bus and voltage regulation via on-load tap-changing transformers.
? OATS aims to fill this gap and provide a platform for extending the classical power systems optimisation models.
? Such constraints can be included in the existing UC implementation of OATS by modifying the constraints within the model file using the method described in the online documentation.
? The most important features of OATS are its fast learning curve, ease of extending existing models and no third-party dependence on commercial products.
DISADVANTAGE :
? The optimal power flow is a non-linear and non-convex problem which was introduced in the sixties for the first time.
? Although it is considered to be a classic power systems problem among researchers, depending on the technical applications and operational dimensions, it may be adapted to various versions.
? Many researchers have been trying to either simplify MPOPF by linearising the main problem, or by making it more reliable by finding the global optimum point with different convex relation approaches such as, semidefinite programming (SDP) relaxations and second-order cone programming
PROPOSED SYSTEM :
• Two test cases are presented to assess the performance of the proposed method. They are both based on a modified version of the Polish 3012wp network, which can be found in .
• This system has been augmented with both wind nodes and storage devices. Three hundred storage devices were placed at randomly chosen locations within the network.
• This short-sighted operation of storage leads to a higher operating cost over the entire day, compared to the time-varying scheme proposed.
• Conversely, when ? is increased to 105 or 106, the storage usage pattern matches the reference pattern more closely.
ADVANTAGE :
? Though reliability is the most critical factor when it comes to the global optimum solution, computational performance is pivotal for the implementation and application of an algorithm for online operational purposes.
? They concluded that option (C) has more efficient computational performance than options (A) and (B); therefore, we use option (C) formulation to apply the most efficient mathematical formulation with respect to the implementation of storage devices.
? Sparsity structure of analytical derivatives is extracted in order to increase the performance in terms of memory requirements and sparsity calculations in different loops.
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