This paper proposes a novel power compensation algorithm in three-phase four-wire systems by using p-q-r theory. The p-q-r theory is compared with two previous instantaneous power theories, p-q theory and cross vector theory. The p-q-r theory provides two-degrees of freedom to control the system currents by only compensating the instantaneous imaginary power without using any energy storage element. The definition of powers maintains power conservation, and agrees well with the general understanding of power. Simulation results show the superiority of p-q-r theory both in definition and compensation.

This paper proposes a novel structure for Industrial Demand Response Aggregators (IDRA) to provide operational flexibility for the power system. A robust self-scheduling approach is formulated for the first time to optimize different sub-processes of the whole production line of heavy industries. The new approach satisfies the customer order with the lowest energy cost. Numerical studies are implemented on 8 integrated cement factories, from Khorasan Regional Electric Company (KREC), in the east of Iran. The results show that the integrated model of heavy industries provides guaranteed flexibility to the system when a power shortage occurs or system reliability is jeopardized.

The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.

In this paper, a model-based predictive control method is proposed for utilization of flexible resources such as battery energy storage systems and heating systems effectively to provide demand response in low-voltage distribution networks with solar PV. The contributions of this paper are twofold. Firstly, a linear power flow method based on relaxation of branch power losses applicable to radial distribution networks is proposed and formulated. Secondly, a flexible resources controller which solves a multi-objective linear optimization problem in recedinghorizon fashion is formulated taking into account system states, forecasts of generation and loads. Using the proposed control algorithm, flexibility from network resources can be utilized for low-voltage network management with assurance of quality of service to the customers. Simulations are conducted for summer and winter cases on a simplified Danish low-voltage network using Matlab/Simulink to study the performance of the proposed control method. Compared to the methods in state of the art, the proposed linear power flow method is proven to be accurate for the calculation of network power flows. Simulation results also show that proposed flexible resources controller can meet the network control objectives while satisfying the network constraints and operation limits of the flexible resources.

The integration of renewable energy sources (RESs) is rapidly increasing within energy systems worldwide. However, this shift introduces intermittency and uncertainty on the supply side. To hedge against RES intermittency, demand-side flexibility introduces a practical solution. Therefore, further studies are required to unleash demand-side flexibility in power systems. This flexibility is relevant across various sectors of power systems, including residential, industrial, commercial, and agricultural sectors. This paper reviews the key aspects of demand-side flexibility within the residential sector. To achieve this objective, a general introduction to demand flexibility across the four sectors is provided. As a contribution of this paper, and in comparison with previous studies, household appliances are classified based on their flexibility and controllability. The flexibility potential of key residential demands, including heat pumps, district heating, electric vehicles, and battery systems, is then reviewed. Another contribution of this paper is the exploration of demand-side flexibility scheduling under uncertainty, examining three approaches: stochastic programming, robust optimization, and information-gap decision theory. Additionally, the integration of demand flexibility into short-term electricity markets with high-RES penetration is discussed. Finally, the key objective functions and simulation software used in the study of demand-side flexibility are reviewed.