Consumption Rate Of Renewable Energy Research Articles

Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination

Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives, the adjustment of load demand curves is carried out considering the demand side response (DSR) on the load side. The complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source side. In the network side, the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use (TOU) price and sell the surplus power of renewable energy to the power grid for profits. The improved memetic algorithm (IMA) is introduced and applied to solve the established mathematical model. The promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.

Coordinated Dispatch of Integrated Energy Systems Considering the Differences of Multiple Functional Areas

A large amount of wind turbine power and photovoltaic power is abandoned in many areas with abundant renewable energy due to thermal-electric coupling, inadequate local consumption capacity, and limited capacity of transmission lines, etc. To solve the above problems, a coordinated dispatching method for integrated energy systems is proposed in this paper. Firstly, the spatiotemporal characteristics of diversified loads in multiple functional areas are introduced, including the inertia and elasticity of heating/cooling loads, the spatiotemporal distribution of electric vehicles, and the optimum transmission distance of diversified loads, etc. Secondly, a coordinated dispatching model of integrated energy systems is proposed, which considers the differences of multiple functional areas and various forms of energy systems. Finally, an actual distribution system in Jianshan District, Haining, Zhejiang Province of China is investigated for demonstrating the effectiveness of the proposed model. The results illustrate that the proposed model could effectively improve the consumption rate of renewable energy and reduce the volatility of renewable energy by considering the coordination of electric vehicles, tie lines, and heating/cooling systems in multiple functional areas.

Smart Community Energy Cost Optimization Taking User Comfort Level and Renewable Energy Consumption Rate into Consideration

With the rapid development of smart community technologies, how to improve user comfort levels and make full use of renewable energy have become urgent problems. This paper proposes an optimization algorithm to minimize daily energy costs while considering user comfort level and renewable energy consumption rate. In this paper, the structure of a typical smart community and the output models of all components installed in the community are introduced first. Then, the characteristics of different types of loads are analyzed, followed by defining the coefficients of user comfort level. In this step, the influence of load-scheduling on user comfort level and the renewable energy consumption rate is emphasized. Finally, based on the time-of-use gas price, this paper optimizes the daily energy costs for an off-grid community under the constraints of the comfort level and renewable energy consumption rate. Results show that scheduling transferable loads and interruptible loads are not independent to each other, and improving user comfort level requires spending more money as compensation. Moreover, fully consuming renewable energy has side effects on energy bills and battery lifetime. It is more conducive to system economy and stability if the maximum renewable energy consumption rate is restricted to 95%.

A bi-level planning approach for hybrid AC-DC distribution system considering N-1 security criterion

Renewable energy is widely distributed in remote or coastal areas. In order to improve the economic efficiency and the consumption rate of renewable energy in distribution system, such distributed generators can be connected through a DC network, which provides power supply for AC system through collection lines. This paper presents a bi-level planning model for AC-DC distribution system with consideration of N-1 contingency. The upper-level model optimizes the total investment costs and operating costs in both AC and DC system over the planning horizon. The lower-level model aims to improve the reliability of the DC system by minimizing curtailment cost of wind farm and photovoltaic under the worst N-1 contingency. Dual technology and Big-M method are applied to reformulate lower-level model as a robust optimization. A novel solving strategy is proposed by combining modified genetic algorithm and numerical method in a nested way. Case studies illustrate that the proposed planning approach is more reliable in dealing with N-1 contingency and more effective in solving large-scale optimization compared with the existing planning approach.

Cost-Aware Renewable Energy Management With Application in Cellular Networks

In this paper, we propose a cost minimization strategy for facilities with shared access to a renewable energy farm. We assume space-time varying electricity prices, and model the facilities as non-deferrable time-varying loads. A practical application of such an arrangement can be found in cellular networks, where operators can deploy a centralized energy farm to (partially) power their base stations in a given geographical area. Unlike existing works, we take into account the non-linear behavior of the battery at the energy farm, and obtain closed-form results by solving the corresponding optimization problem in continuous time . We also derive a closed-form expression which estimates the maximum cost saving in terms of the total energy harvested across the entire planning horizon. The results presented in this paper can be used to devise and benchmark low-complexity online strategies, and to understand the relationship between the maximum cost saving, the optimal consumption rate of renewable energy, and system parameters such as battery efficiency and nominal output power.