The increasing uptake of distributed energy resources (DERs) in distribution systems and the rapid advance of technology have established new scenarios in the operation of low-voltage networks. In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions. Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario. A major challenge to implementing P2P energy trading is that of ensuring that network constraints are not violated during the energy exchange. This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints. The proposed method is tested on a typical UK low-voltage network. The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture.

This paper presents an application of a two-point estimate method (2PEM) to account for uncertainties in the optimal power flow (OPF) problem in the context of competitive electricity markets. These uncertainties can be seen as a by-product of the economic pressure that forces market participants to behave in an "unpredictable" manner; hence, probability distributions of locational marginal prices are calculated as a result. Instead of using computationally demanding methods, the proposed approach needs 2n runs of the deterministic OPF for n uncertain variables to get the result in terms of the first three moments of the corresponding probability density functions. Another advantage of the 2PEM is that it does not require derivatives of the nonlinear function used in the computation of the probability distributions. The proposed method is tested on a simple three-bus test system and on a more realistic 129-bus test system. Results are compared against more accurate results obtained from MCS. The proposed method demonstrates a high level of accuracy for mean values when compared to the MCS; for standard deviations, the results are better in those cases when the number of uncertain variables is relatively low or when their dispersion is not large

In the paper, the Tellegen's theorem and adjoint networks are used to derive a new, local voltage-stability index. The new approach makes it possible to determine the Thevenin's parameters in a different way than adaptive curve-fitting techniques, from two consecutive phasor measurements. The new index was rigorously tested on different test systems. The results were obtained on a static two-bus test system and on the dynamic Belgian-French 32-bus test system that includes full dynamic models of all power-system components crucial to the voltage instability analysis. The results show advantages of the proposed index: it is simple, computationally very fast, and easy to implement in the wide-area monitoring and control center or locally in a numerical relay

In light of the heightened uptake of distributed energy resources, distributed methods are attracting increased focus. This paper proposes an adaptive scheme to improve the convergence of ADMM on the component-based dual decomposition of the AC OPF. The method incorporates a local curvature approximation scheme, with an underlying parameter update steps inspired by the local residual balancing scheme, to automatically tune the penalty parameters locally, without central oversight. Substantial speed-ups over the vanilla ADMM, as high as 10.6 times faster, are demonstrated on real-world test systems with more than 13600 buses, as well as on other difficult test cases. Furthermore, scalability is assessed by showing that, unlike in the centralized computation, the distributed implementation of the proposed adaptive method is independent of system size.

This paper proposes a novel strategy for participation of variable speed wind turbines in primary frequency control. The proposed strategy is based on the deployment of the kinetic energy of the rotating masses to reduce the need for deloaded operation while still being able to provide the required power reserve for the defined time frame. A steady-state deloaded operating point is optimized with respect to the amount of kinetic energy stored in the rotating masses. The parameters of the algorithm are calculated offline and stored in lookup tables. The proposed strategy is tested using a modified Nordic 32-bus test system for various operating scenarios. The results show that part of the primary frequency control power reserve of the thermal units can be replaced by the reserve provided by wind power, while retaining the desired frequency response following a disturbance. A comparison with the conventional deloaded strategy shows that between 1.16% and 2.79% energy savings can be achieved on a yearly basis, depending on the wind regime of the sites analyzed.

A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an aggregator that aims to minimize the cost of electricity purchased in a pooled wholesale market. This paper presents a fast distributed algorithm for aggregating a large number of households with a mixture of discrete and continuous energy levels. A distinctive feature of the method in this paper is that the nonconvex demand response (DR) problem is decomposed in terms of households as opposed to devices, which allows incorporating more intricate couplings between energy storage devices, appliances, and distributed energy resources. The proposed method is a fast distributed algorithm applied to the double smoothed dual function of the adopted DR model. The method is tested on systems with up to 2560 households, each with 10 devices on average. The proposed algorithm is designed to terminate in 60 iterations irrespective of system size, which can be ideal for an on-line version of this problem. Moreover, numerical results show that with minimal parameter tuning, the algorithm exhibits a very similar convergence behavior throughout the studied systems and converges to near-optimal solutions, which corroborates its scalability.