Nian Liu

Bio: Nian Liu is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Energy management & Demand response. The author has an hindex of 28, co-authored 101 publications receiving 3043 citations.

Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers

Abstract: According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy.

Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach

Abstract: For microgrids with photovoltaic (PV) prosumers, the effective energy sharing management (ESM) is important for the operation. In this paper, a Stackelberg game approach for ESM is proposed. First, according to feed-in-tariff of PV energy, a system model of ESM is introduced, which includes the profit model of microgrid operator (MGO) and the utility model of PV prosumers. Moreover, an hour-ahead optimal pricing model of ESM is proposed. The model is designed based on Stackelberg game, where the MGO acts as the leader and all participating prosumers are considered as the followers. With the proof of equilibrium and uniqueness of the Stackelberg equilibrium, the MGO is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a billing mechanism is designed to deal with the uncertainty of PV energy and load consumption. By using the collected data from realistic PV-roofed buildings, the effectiveness of the model is verified in terms of the profit of MGO, the utilities of prosumers, and the net energy of the microgrid.

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

Abstract: 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 MGO's prices and optimizing net load characteristic, etc.

A Heuristic Operation Strategy for Commercial Building Microgrids Containing EVs and PV System

Abstract: Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate.

Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game

Abstract: According to the energy policy, which encourages local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers is proved to be a more effective way compared with independent operations of each prosumer. In this paper, an energy storage (ES)-equipped energy-sharing provider (ESP) is proposed to facilitate the energy sharing of multiple PV prosumers. With the help of the ESP, the autonomous PV prosumers can be formed as an energy-sharing network, and the energy-sharing activities can be categorized as direct sharing and buffered sharing. First, with the assistance of the ES, a day-ahead scheduling model of the ESP is built to increase the operation profit and improve the net power profile of the energy-sharing network, which considers the uncertainty of PV energy, electricity prices, and prosumers’ load via stochastic programming. Moreover, to further increase the energy sharing, a real-time demand response model based on a Stackelberg game is presented to coordinate the energy consumption behavior of prosumers by using internal prices. Finally, through a practical case study, the effectiveness of the method is verified in terms of improving the economic benefits and PV energy sharing.

Review: A state-of the-art survey of TOPSIS applications

Abstract: Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.