Nian Liu

Energy Management for Joint Operation of CHP and PV Prosumers Inside a Grid-Connected Microgrid: A Game Theoretic Approach

This paper mainly focuses on the energy management of microgrids (MGs) consisting of combined heat and power (CHP) and photovoltaic (PV) prosumers. A multiparty energy management framework is proposed for joint operation of CHP and PV prosumers with the internal price-based demand response. In particular, an optimization model based on Stackelberg game is designed, where the microgrid operator (MGO) acts as the leader and PV prosumers are the followers. The properties of the game are studied and it is proved that the game possesses a unique Stackelberg equilibrium. The heuristic algorithm based on differential evolution is proposed that can be adopted by the MGO, and nonlinear constrained programing can be adopted by each prosumer to reach the Stackelberg equilibrium. Finally, via a practical example, the effectiveness of the model is verified in terms of determining MGOs prices and optimizing net load characteristic, etc.

Autonomous Energy Management Strategy for Solid-State Transformer to Integrate PV-Assisted EV Charging Station Participating in Ancillary Service

Photovoltaic-assisted charging station (PVCS) is expected to be one of the important charging facilities for serving electric vehicles (EVs). In this paper, a type of solid-state transformer (SST) is introduced to the PVCS design and an autonomous energy management strategy (EMS) for SST is proposed. This study aims to develop an effective real-time EMS for PVCS participating in ancillary service of smart grid, and the rule-based decision-making method is utilized. Considering the dynamic classification of EVs, an energy-bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible resources. Moreover, considering the EBC results and power command from the aggregator, a charging power allocation algorithm is designed for power distribution of flexible EVs. By case study and experiment analysis, the proposed EMS is effective in real-time energy management and suitable for practical applications.

Optimal power utilizing strategy for PV-based EV charging stations considering Real-time price

Developing EVs has been established as an effective way to reduce carbon emission in development and ensure energy security by many countries. The PV-based EVCS (EV charging station) can efficiently alliviate the charging pressures of the distribution network and reduce EVs indirect emissions. A Real-time power utilizing strategy considering real-time price for PV-based EV charging station is proposed to allow EVs to participate in DR (Demand Response) and charge economically. The DR is adopted for power utilizing strategy to offer a kind of financial benefits for EVs and minimize the charging fees. The behavior mode and the CRFR mode are utilized to figure out the EV demand mode. The PSO (Paticle Swarm Optimal) algorithm is utilized to optimally allocate the charging power. The real-time price is forecasted by wavelet neural network based on the historical data and the total charging power responses to the real-time price so as to minimize charging cost. A case study is simulated to validate the proposed method.

Optimal Operation Method for Microgrid with Wind/PV/Diesel Generator/Battery and Desalination

The power supply mode of island microgrid with a variety of complementary energy resources is one of the most effective ways to solve the problem of future island power supply. Based on the characteristics of seawater desalination system and water demand of island residents, a power allocation strategy for seawater desalination load, storage batteries, and diesel generators is proposed with the overall consideration of the economic and environmental benefits of system operation. Furthermore, a multiobjective optimal operation model for the island microgrid with wind/photovoltaic/diesel/storage and seawater desalination load is also proposed. It first establishes the objective functions which include the life loss of storage batteries and the fuel cost of diesel generators. Finally, the model is solved by the nondominated sorting genetic algorithm (NSGA-II). The island microgrid in a certain district is taken as an example to verify the effectiveness of the proposed optimal method. The results provide the theoretical and technical basis for the optimal operation of island microgrid.

A Robustness-Oriented Power Grid Operation Strategy Considering Attacks

Power system operation is facing increasing cyber and physical attack risks, so it becomes pressing to develop effective methods to improve the robustness of electric power infrastructure in the presence of significant attacks. As it is not guaranteed that attacks can always be detected and thwarted before they cause disturbances and damages to the power systems, increased robustness can contribute to reducing the consequence of attacks. In this paper, a holistic robustness framework is proposed by extending the conventional security-constrained optimal power flow (SCOPF) analysis to incorporate the risk caused by attacks. The corresponding solution methodology is proposed by combining particle swarm optimization and primal-dual interior point methods. Case studies conducted based on several test systems demonstrate that the proposed SCOPF model is able to reduce the consequence of attacks. This paper can provide some insight into improving the power system operation robustness in the face of significant attacks.

Coordinated attacks against power grids: Load redistribution attack coordinating with generator and line attacks

With the increasing terrorism and sabotage activities, the power grid is becoming more vulnerable to various kinds of cyber and physical attacks. The coordination between the attacks could bring disastrous impacts. In this paper, two typical attack coordination scenarios are studied: the coordination between load redistribution (LR) attack and generator attack; and the coordination between LR attack and line attack. They are formulated as bilevel optimization problems, where the attacker in the upper level aims to maximize the load curtailment while the defender in the lower level makes effort to reduce the load curtailment. The case studies conducted based on an IEEE 14-bus system indicate that when attacking the measurements and essential generation/transmission elements in a coordinated manner, the attacker could maximize the damage with the limited attack resource by disrupting the physical system and misleading the power dispatch simultaneously. This study can provide meaningful insights on how to prevent and mitigate such high-impact, low-frequency (HILF) coordinated attacks.

Optimal Configuration for Batteries and Chargers in Battery Switch Station Considering Extra Waiting Time of Electric Vehicles

AbstractBattery switch stations (BSSs) are regarded as important infrastructure for the development of electric vehicles (EVs). However, studies on the optimal configuration of BSSs are still limited. In order to assist the design of BSS, an optimal configuration framework based on operational planning method is proposed. An optimal model is formulated to minimize investment, maintenance, and electricity cost of BSSs. The extra waiting time (EWT) of EVs is taken as a constraint for service quality. A simulation model for operations of a BSS is designed and the states of EVs and batteries under different operation strategies can be simulated so the EWT for each EV can be calculated. Furthermore, differential evolution (DE) is used to solve the optimal configuration model. Finally, the actual data and parameters of electric taxis and electric buses are used for the case study. Results show the quantitative relations among EVs, batteries, and chargers under different levels of average EWT can be obtained. Th...

Real-time energy management algorithm for PV-assisted charging station considering demand response

Photovoltaic (PV)-assisted charging station is one of important charging facilities served for Electric Vehicles (EV). To minimize the operation cost of photovoltaic (PV)-assisted electric vehicle (EV) charging station, an energy management considering demand response (DR) strategy is proposed. The wavelet neural network (WNN) is utilized to forecast the price based on history data and the forecasting result is regarded as the basic price vector. Real-time price is utilized to replace basic price in current time slot to form the new price vector (NPV). The feasible energy demand region (FEDR) model is utilized to calculate the lower bounds and upper bounds dynamically. The dynamic linear programming (DLP) algorithm is utilized to calculate the optimal charging energy schedule based on the NPV and FEDR model. A comprehensive result obtained from comparison simulations has shown that the proposed ADR strategy is excellent in reducing cost, improving PV self-consumption and mitigating charging peak load on grid.

An online algorithm based on Lyapunov optimization for energy management of household micro-grids

In this paper, an online scheduling algorithm based on Lyapunov optimization for smart energy management in household micro-grids is proposed. Household appliances are classified into baseline load and controllable load. The consumption power of controllable appliances Electric Water Heater (EWH), Heating, Ventilation and Air Conditioning (HVAC) and Pure Electric Vehicle (PEV) are set as decision variables. To reduce the electricity bill cost of users, an allocation strategy of PV power based on the maximum delay time and priorities of the three appliances is introduced. Based on the pre-allocated PV power of every appliance, Lyapunov optimization method is applied to solve the energy management problem with an efficient online electricity scheduling algorithm, in which three controllable demand queues are introduced. The algorithm does not rely on future information and is implemented in an online fashion, which could adapt to the variable and unpredictable information well. Our simulation study validated the superior performance of the proposed approach.

A Method Based on Wavelet Neural Network for Power System Short Term Load Forecasting

The short-term load of Power System is uncertain and the daily-load signal spectrum is continuous. The approach of Wavelet Neural Network (WNN) is proposed by combing the wavelet transform (WT) and neural network. By the WT, the time-based short-term load sequence can be decomposed into different scales sequences, which is used to training the BP neural network. The short-term load is forecasted by the trained BP neural network. Select the load of a random day in Lianyungang to study, according to the numerical simulation results, the method proves to achieve good performances.