As the proportion of renewable energy sources (RESs) increases, active distribution network (ADN) operation becomes a more challenging problem due to unpredictable nature of RESs. To address this problem, a new dynami...
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As the proportion of renewable energy sources (RESs) increases, active distribution network (ADN) operation becomes a more challenging problem due to unpredictable nature of RESs. To address this problem, a new dynamic economic dispatch (DED) for ADN is proposed which can determine the optimal scheduling results based on cost/benefit analysis. Intervals with variable endpoints are applied to describe the nodal net load, which is also named as admissible region in this paper. The admissible region of nodal net load and other scheduling results are simultaneously determined by striking a balance between operation cost when net load varies within the corresponding intervals and benefit represented by reduction of operation risk when net load varies out of their intervals. To facilitate the solution, a new injection shift factor (ISF) of the linearized distflow branch equations is proposed, and the voltage magnitude and reactive power can be well addressed, which especially fits for ADN. A surrogate affine policy is applied to avoid the introduction of bilinear terms. And a new step-superimposing linearization method for risk cost is proposed by exploring the specific characteristics of risk cost expression, then binary variables can be eliminated in the model. Finally, this model is recast as a linear programming problem and the computation efficiency can be guaranteed. The validity and effectiveness of the proposed model are illustrated on the IEEE 33-node and 69-node distribution systems.
We consider a dynamic model of electric vehicle (EV) charging in a power distribution grid. We introduce uncertainty in the demand side, arising through consumer behavior, as well as the supply side, to account for in...
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ISBN:
(数字)9781665405577
ISBN:
(纸本)9781665405577
We consider a dynamic model of electric vehicle (EV) charging in a power distribution grid. We introduce uncertainty in the demand side, arising through consumer behavior, as well as the supply side, to account for intermittent renewable energy sources (RES). By developing a fluid approximation, we show that the invariant point of the stochastic process can be obtained by solving an AC-OPF problem with an exact convex relaxation, thereby creating a deterministic and computationally efficient analysis of the charging capacity of the network. We illustrate the accuracy of our fluid approximation by detailed simulation studies. Next to that, we use the model to assess both the impact of RES generation and the physical location of RES in the network. This way, we create a measure for the positioning of additional RES or local energy storing systems.
We consider a distribution grid used to charge electric vehicles (EVs) such that voltage drops stay bounded. We model this as a class of resource-sharing networks, known as bandwidth-sharing networks in the communicat...
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We consider a distribution grid used to charge electric vehicles (EVs) such that voltage drops stay bounded. We model this as a class of resource-sharing networks, known as bandwidth-sharing networks in the communication network literature. We focus on resource-sharing networks that are driven by a class of greedy control rules that can be implemented in a decentralized fashion. For a large number of such control rules, we can characterize the performance of the system by a fluid approximation. This leads to a set of dynamic equations that take into account the stochastic behavior of EVs. We show that the invariant point of these equations is unique and can be computed by solving a specific AC optimal-power-flow problem (ACOPF), which admits an exact convex relaxation. We illustrate our findings with a case study using the SCE 47-bus network and several special cases that allow for explicit computations.
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