Long-term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions based on predictions help substantially reduce the power grid infrastructure costs. The classical approach of LTLF is limited to the usage of artificial neural networks (ANN) or regression-based approaches along with a large set of historical electricity load, weather, economy and population data. Considering the drawbacks of classical methods, this paper introduces a novel sequence to sequence hybrid convolutional neural network and long short-term memory (CNN-LSTM) model to forecast the monthly peak load for a time horizon of three years. These drawbacks include, lack of sensitivity to changing trends over long time horizons, difficulty of fitting large number of variables and complex relationships, etc. [1]. Forecasting time interval plays a key role in LTLF. Therefore, using monthly peak load avoids unnecessary complications while providing all essential information for a good long-term strategical planning. The accuracy of the proposed method is verified by the load data of “New South Wales (NSW)”, Australia. The numerical results show that, proposed method has achieved higher prediction accuracy compared to the existing work on long-term load forecasting.

Nowadays, enabled by current smart grid technology, electricity consumers can play an active role in providing Ancillary Service (AS) as a type of Demand Response (DR). Participating AS can assist stabilizing the power grid by following the Frequency Regulation Signal, or RegD signal in this study whilst receiving economic benefits. Industrial loads are an indispensable component as a demand-side regulating resource of ancillary service due to their intensive electricity consumption. In this paper, we use grid-connected solar Photovoltaics (PV) panels combined with Energy Storage System (ESS) to produce continuous electricity consumption signals in order to follow the RegD signal. The participation of solar energy in real-time regulation provision process is emphasized, which is modeled based on a variety of operation modes in accordance to Australian Standard. Through a particular case study, with the integration of solar energy the proposed method poses cost-effectiveness in industrial plant scheduling and a favorable load following capability, helping ensure the frequency stability of the electric grid. The proposed methodology is more economically advantageous compared to identical industrial loads only equipped with onsite ESS, and requires less switchings on machines compared to industrial plants with passive use of solar energy.

In this paper, a fully decentralized adaptive droop optimization strategy for inverters is proposed for minimizing power loss during power transmission for islanded microgrids integrated with solar photovoltaic (PV) systems. From a hierarchical point of view, the proposed control architecture of the DGs encompasses both the primary and secondary levels, in a decentralized plug-and-play manner. The primary level, realized by a droop controller, is in charge of the fast response in load-sharing among all the generation units in the microgrid. The secondary level control, with a larger operation time scale, has the objective of minimizing the power loss in the system, which is achieved by the proposed adaptive droop functions. The advantage of the proposed droop optimization strategy is that the power generation units are fully decentralized by using only local measurements. The adaptability of the droop functions is achieved by adopting a Perturbation and Observation (P&O) method. The system converges to a steady state with minimum power loss. The process within the secondary controller continues indefinitely and any changes in loads or grid configuration will bring the system to a new steady state with minimum power loss.

In this paper, we propose a novel control strategy for doubly fed wind turbine generators (DFWTG) in complex power systems to improve the primary frequency response and enhance low-frequency oscillation damping of power systems. The main innovation in the new control scheme dwells in the novel control schemes for rotor side controller (RSC) of DFWTG. Weighted frequency deviations of local synchronous generator (SG) bus-bars are utilized as input signals to a dedicated power system stabilizer (PSS), specifically designed for the RSC of DFWTG, with parameters optimized by particle swarm optimization (PSO). The newly devised RSC with conventional DFWTG control structure is capable of ameliorating primary frequency response of the power system. To eliminate the area control error (ACE), a secondary control scheme is incorporated, which makes use of the spinning reserve of selected synchronous generators through automatic generation control (AGC). Tie-line power deviations are employed as control signals in both primary and secondary control schemes, on the purpose of further enhancing the primary and secondary frequency regulations and also maintaining the obligation of power transmissions among adjoining areas. Simulation results demonstrate the superiority of the proposed DFWTG control methods in enhancing primary frequency response and also suppressing low-frequency oscillations (LFO) of the power system over the conventional strategy.

This paper proposes a load-oriented control parameter optimization strategy for STATCOM to enhance low-frequency oscillation damping (LFOD) and improve stability of overall complex power systems. Frequency deviations of generators of interest are employed as the input signals of the designed supplementary damping controller of STATCOM. In order to obtain the optimal load-oriented control parameters, a day-ahead load-forecasting scheme is devised, using artificial neural network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique by minimizing the critical damping index (CDI). The proposed control strategy is implemented in an IEEE standard complex power system, and the numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller. Compared to conventional robust controller with universal parameters, this novel load-oriented optimal control strategy shows its superiority in alleviating LFOs and enhancing the overall stability of the power system. Since the proposed control scheme aims to adaptively adjust the controller parameters in correspondence to load variations, this study is envisaged to have practical utilizations in industrial applications.

This paper introduces a novel application of a stochastic filtering algorithm-unscented particle filter (UPF)-to estimate the inaccessible state variables of doubly fed induction generator (DFIG) connected to a multi-area power system with local phasor measurement units (PMUs). This dynamic estimation implementation bears more advanced features than the particle filter (PF) method since it can not only track the dynamic states more accurately and smoothly when the power system experiences sudden disturbances, but also manage to resolve the particle degeneration problem that exists in the PF algorithm. Moreover, the proposed UPF-based dynamic state estimation method is achieved in a decentralized manner and only uses local PMU measurements of voltage and current. Through a comparison study where popular stochastic filtering methods, unscented Kalman filter (UKF) and PF, are employed to achieve the same estimation purpose, this paper shows the superiority of the UPF algorithm particularly designed for state estimation over the other two existing algorithms in terms of accuracy and error tolerance.