High Photovoltaic Power Penetration Research Articles

Demand Side Management in Smart Grid: A Dynamic-Price-Based Demand Response Model

Demand side management (DSM) is an important way to achieve smart energy management. Herein, a dynamic price (DP)-based demand response (DR) model is developed for DSM in smart grid. The proposed DR model can shift the peak electricity demand, thereby improving the power system stability and reliability. In a district-scale smart grid with a high photovoltaic power penetration, the energy service provider (ESP) optimizes the DP to maximize its utilities and reduce the load fluctuation while minimizing the bills and dissatisfaction of electricity consumers (ECs). The game theory model is used to explore the interaction between ESP and ECs, and the existence of Nash equilibrium is proved. The proposed DR model is validated with real-world data from a commercial and residential cluster in Suzhou City, Jiangsu Province, China. The results show that the peak electricity demands of commercial and residential ECs decreased by 4.99% and 9.99%, respectively, through the proposed DR model. Meanwhile, the ESP's net profits increased by 7.13% and 2.37%, respectively, while ensuring the ECs’ benefits. The results also demonstrate that the proposed DR model is robust in different scenarios. This article contributes to the effectiveness and efficiency of energy engineering management.

The Effect of the Fast Frequency Response of the Solar Energy System on the Power System Dynamic Frequency Evolution

The existence of a large-scale photovoltaic power generation system without any rotational inertia can deteriorate the frequency stability of the power system. The solar energy storage system consisted of photovoltaic power generation and the energy storage device with fast charge and discharge can provide fast frequency responses to the grid through the power electronics controls, and therefore improve the dynamic frequency security of the low-inertia system. This paper discusses the modeling and fast frequency controls, namely virtual inertia and droop control. The modified WSCC 9-bus system with high photovoltaic power penetration and low inertia serves to show the effects of the solar energy storage system’s fast frequency responses on the frequency evolutions of the new power system. The results show that in the low inertia system with large-scale photovoltaic power generation, the active power droop control of the solar energy storage system can decrease the frequency deviations, but may introduce low-frequency oscillations during the frequency evolution following a contingency; the virtual inertia control can depress both the rate of change of frequency as well as the oscillations during the frequency evaluation.

Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing

The number of grid-connected large-scale solar photovoltaic (PV) power plants has increased significantly in the last 10 years, which results in high PV power penetration into the grid. Especially for the wide-area spatially distributed countries, power ramp in one PV plant can be balanced with another PV power plant generation. This has been studied in the literature for short term horizons for high-frequency data. In this study, hourly simulation data are analysed by Kendall's correlation coefficient, unsupervised and rule-based machine learning algorithms for spatio-temporal operational balancing constraints. Association rules generated by using the Apriori algorithm provide power ramp direction maps for Spatio-Temporal analysis. The K-means clustering (based on the Hartigan-Wong algorithm) is used for unsupervised learning application for the spatio-temporal relations for solar PV ramp zones.The proposed model can be used as a fast and effective decision-making tool with qualitative results for the system operator with minimal expert knowledge, while they can be also integrated to optimal power flow analysis constraints. This study introduces the first successful application of association rules integrated with the K-means technique for the Solar PV spatio-temporal balancing purpose to the best of our knowledge.

Reliable Islanded Microgrid Operation Using Dynamic Optimal Power Management

The tradeoff between the operating cost and the reliability of microgrids imposes challenges of having cost-effective power dispatch and reliable operations. The work presented in this article optimally solves this issue in a microgrid that contains photovoltaic (PV), dispatchable generator, and storage battery. Flexible control algorithms are developed to enable high penetration of PV power in islanded microgrids such that the challenge of having a cost-effective and reliable operation can be mitigated. This is achieved by creating a power flow optimization framework that considers all variables associated with the system operation. Particle swarm optimization is utilized to effectively optimize the power flow among the integrated sources and to dynamically schedule the battery state of charge. The proposed controller of each source works to fulfill the requirements of the optimization. The energy index of reliability is employed as a direct measure to quantify the reliability level of the microgrid. The effectiveness of the proposed approach is demonstrated by simulations and validated by experimental studies.

Cooperative control of photovoltaic based water pumping system

In this study, novel control schemes are developed for cooperative control of stand-alone and grid-connected photovoltaic (PV) based water pumping system. Brushless DC motor is used as pumping motor due to its superior characteristics. The cooperative control effectively utilises the available resources. In stand-alone system, control scheme is developed for power sharing among PV pumping units. Assuming some of the PV units have battery, the charging/discharging controller is incorporated. In grid-connected system, flexible control scheme is proposed so that consumer can draw lesser power from grid by running the motor at lower speed instead of rated speed. In case of high PV power penetration into weak grid, the modified perturb and observe algorithm is proposed for sharing of de-loaded power among the PV units. Due to de-loading of PV power, motor speed reduces, hence, speed compensation mechanism is developed to bring back the speed at desired value. As a futuristic concept, consumer can buy power from neighbour instead of taking from grid. To establish above-mentioned concept, control scheme is developed for sharing the power among PV pumping units. The simulation and hardware results are carried out to test performance of the proposed control schemes.

Solar Power Forecasting Based on Domain Adaptive Learning

Solar power forecasting is critical to ensure the safety and stability of the power grid with high photovoltaic power penetration. Machine learning methods are compelling in solar forecasting. These methods can capture the complex coupling relationship between different meteorological factors without physical modeling. Most of the existing machine learning based forecasts follow the batch learning manner. Once the training is completed, the structure and parameters of the model are usually no longer adjusted. However, the climate is complex and dynamic. It is difficult for a fixed model to adapt to the climate characteristics of different regions or periods. Therefore, an online domain adaptive learning approach is proposed in this paper. Knowledge can be selectively accumulated or forgotten in its iterative process. As weather changes, the model can dynamically adjust its structure to adapt to the latest weather conditions. Unlike existing adaptive iterative methods, the proposed adaptive learning approach does not rely on the labels of the test data in the updating process. Experiments show that this method can effectively track changes in data distribution and obtain reliable prediction results.

MPC based control strategy for battery energy storage station in a grid with high photovoltaic power penetration

The AGC (automatic generation control) reserve capacity requirement in a gird with high photovoltaic (PV) power penetration is much higher than that in a traditional grid in order to address the rapid PV power fluctuation, which also means a higher operating cost of the power grid. In contrast with the dispersed energy storage units located in PV plants, the integration of battery energy storage station (BESS) in a power grid can effectively mitigate the PV power fluctuation and decrease the AGC reserve capacity, reducing the operating cost from the aspect of the power grid operator. However, currently BESS is still an expensive option in view of the high price per unit size. Consequently, the determined BESS with size-limited capacity needs to be fully utilized to improve the economic performance of both BESS and the power grid. For this reason, a novel model prediction control (MPC) based control strategy for BESS is presented in this paper, aiming to minimize the equivalent operating cost of BESS during each control step. Specifically, the impact of PV power on AGC reserve capacity and the necessity of BESS in a grid with high PV power penetration are firstly discussed. And then, based on the equivalent cost analysis of BESS, an objective function is presented aiming to minimize the equivalent operating cost of the power gird and BESS during each control period, including the AGC payment and BESS operating cost. Besides, to prolong the lifetime of BESS, a protection measure is presented via the adjustment of BESS charge/discharge power. Afterwards, the application steps of the presented control strategy are presented. Finally, the proposed control strategy is verified using actual PV power data in a grid with high PV power penetration.

Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response

In this paper, we study the behavior of a day-ahead (DA) retail electrical energy market with price-based demand response from air conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a ${Q}$ -learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users’ comfort preferences. Since the retailer agent does not have direct access to the AC loads’ underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer’s decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer’s profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of photo-voltaic power in the distribution feeder.

Evaluation of nodal reliability risk in a deregulated power system with photovoltaic power penetration

Owing to the intermittent characteristic of solar radiation, power system reliability may be affected with high photovoltaic (PV) power penetration. To reduce large variation of PV power, additional system balancing reserve would be needed. In deregulated power systems, deployment of reserves and customer reliability requirements are correlated with energy and reserve prices. Therefore a new method should be developed to evaluate the impacts of PV power on customer reliability and system reserve deployment in the new environment. In this study, a method based on the pseudo-sequential Monte Carlo simulation technique has been proposed to evaluate the reserve deployment and customers' nodal reliability with high PV power penetration. The proposed method can effectively model the chronological aspects and stochastic characteristics of PV power and system operation with high computation efficiency. An auto-regressive and moving average model has also been developed for simulating the chronological characteristics of the solar radiation. Customers' reliability preferences have been considered in the generation and reserve deployment. Moreover, the correlation between PV power and load has been considered in the proposed method. Nodal reliability indices and reserve deployment have been evaluated by applying the proposed method to the Institute of Electrical and Electronics Engineers reliability test system.

96/03901 A low-cost method of evaluationg saving from lighting retorfits

Photovoltaic (PV) power generation is an important component for the future energy system. High penetration of PV power in a power distribution system might however lead to problems with overvoltage and overload. In this study, a method for PV power curtailment and placement of decentralized energy storage is developed to control voltage, feeder currents and distribution substation overloading. The method determines an individual feed-in power limit for each PV system owner based on a voltage-power relationship. Measured data from a 10 kV/400 V three-phase distribution grid in the Swedish municipality of Herrljunga with more than 5000 end-users and simulated PV electricity production data are used for a case study to verify the model. The method is evaluated for yearly PV electricity production of up to 100% of the yearly electricity consumption. The results show that the method is able to prevent overvoltage for all penetration levels in the studied distribution grid, reduce the number of feeders affected by overcurrent and lower the maximum load on the two substations.