Energy management of smart micro-grid with response loads and distributed generation considering demand response

Highlights

• An optimization model based on DR mechanism for micro-grid energy scheduling with loads and DGs.

• TOU tariff and RTP tariff to implement demand response planning for response loads in micro-grid.

• Operation cost under different electricity price policies is the objective function.

• Using the GA approach for minimizing objective functions and determining the best operation strategy.

Abstract

Governmental incentives to develop clean and renewable energy sources, and concerns about the increasingly serious environmental pollution problem, and the development of smart grid technology are the most important motivations for adding distributed generations to conventional power systems and carrying out the Demand Response program (DR). In these circumstances, it has become a key factor for the development of micro-grid to realize efficient and economical operation of smart micro-grid considering Demand Response strategy. In this paper, an intelligent park micro-grid consisting of photovoltaic power generation, combined cooling heating and power system, energy storage system and response load is modeled to study the optimal scheduling strategy of these units by taking into account the price-based demand response. To achieve this goal, an optimization model for the economic operation of micro-grid is established, and the model presented mainly aims to minimize the operating cost of micro-grid system and make full use of clean energy under the premise of considering distributed power generation and demand response. This operation optimization problem is solved by the Genetic Algorithm (GA) and the best solution on the best operating strategy is determined by the clean energy resources and demand response program. Finally, a micro-grid project in China was used to carry out optimization simulation in order to verify the accuracy and reliability of the established model. It is found that the operation optimization model of micro-grid with demand response can effectively reduce the operation cost of and improve the utilization rate of renewable energy sources.

Introduction

Following environmental pollution concerns, increasing clean energy demand, and rush in energy cost, special attention has been recently focused on micro-grid with response loads and distributed generation (Zao and Chen, 2018). As the energy crisis and environmental crisis become more and more serious, renewable energy has been widely concerned and applied to the field of power generation. However, the large-scale direct grid connection of distributed power supply will cause the instability of power system. Under such circumstances, smart micro-grid, as an efficient and safe way to use clean energy, has begun to be developed and applied on a large scale (Rawea and Urooj, 2018).

Smart grid is a new type of power supply mode which integrates renewable energy power generation technology (wind power generation, photovoltaic power generation, biomass energy, tidal energy, etc.), energy management system (EMS) and distribution infrastructure (Wu et al., 2016). It can not only improve energy efficiency and the security of power supply, but also reduce the power loss and the impact on the environment. With the large-scale application of micro-grid technology in distribution system, advanced operation optimization technology has become an important guarantee for micro-grid operation. There are strict requirements for micro-grids access to existing power grids. The connection of the micro-grid cannot affect the stability of the existing power grid. At the same time, the micro-grid should have an independent control system, which can adjust its operation status according to the load and power grid signals independently (Hu et al., 2018).

In the traditional power system, most of the load is uncontrollable and the power consumption is not easy to be measured accurately, which results in that the system can only dispatch power generation resources. Micro-grid, as an efficient power supply mode, integrates distributed resources and loads into one system for dispatching (Husted et al., 2018). Demand Side Management (DSM) can enable different types of loads to actively participate in the optimal operation of the power grid, which is an important means to realize the optimal operation of the micro-grid. As an important way of DSM, Demand Response (DR) program guides users to participate in power grid dispatching through electricity price and incentive information, which makes the economy and stability of micro-grid be improved effectively (Wang et al., 2018). In (Jang et al., 2015) the response index is tested as an alternative to conventional load impact measures and a strong correlation between reductions and cost shares of electricity is identified. The reference (Sun and Li, 2014) establishes a production-power dynamic control model for manufacturing system and explores the potential of power demand reduction of typical manufacturing system by studying Real-time electricity demand response for manufacturing systems. The reference (Sun et al., 2016) establishes the plant-level electricity demand response model considering manufacturing and HVAC systems, and it also models the relation between manufacturing operation and temperature evolution. The aim of the study is identifying an optimal demand response strategy with respect to both production schedule and HVAC control. In (Cominola et al., 2018) the heterogeneity of typical residential water-electricity demand profiles is introduced. Authors contribute a customer segmentation of over 1000 residential accounts in the Los Angeles County and propose recommendations to design customized water-electricity demand management actions. In (Shrouf et al., 2014) a mathematical model to minimize energy consumption cost at machine level is proposed. The proposed algorithm for the model has the potential capability to be integrated into the factory scheduling and its high scalability allows running the production schedule in real time. Authors in (Khan et al., 2018) propose the impact of demand response-DR and electrical energy storage-EES in energy-only market and analysis the impact of limited DR and medium-term EES on a capacity market-CM. Authors in (Lu et al., 2018) propose an artificial intelligence based dynamic pricing demand response algorithm and achieve uncertainty of customer's demand and flexibility of wholesale prices. In (Padmanabhan et al., 2018) a new mathematical model for a Locational Marginal Price (LMP) based, loss included, day-ahead, co-optimized, energy and spinning reserve market including DR provisions, is proposed. A repeated game-theoretic market is proposed in (Motalleb et al., 2018) for selling stored energy in batteries. Day-ahead price signal is improved through a real-time demand response market. The reference (Shahryari et al., 2018) proposes an improved IBDR program which is based on the concept of price-demand elasticity. The proposed method considers the elasticity as a function of consumption time, customer type and peak intensity.

The demand response links the load with the distributed resources, which makes the energy management of micro-grid more reasonable. For the energy management of micro-grid, demand response is only an important factor that needs to be considered. Operation optimization strategy is the necessary means to realize efficient and economical operation of micro-grid. In (Weitzel et al., 2018) model to include battery aging into Micro-grid scheduling problem is presented. Battery aging cost model reduced cycled energy by 37% and increased lifetime by 74%. Authors in (Bellido et al., 2018) state the main barriers to the entry of micro-grids in the world power sector and identify some constraints on promoting their development and participation in the Brazilian Power Sector. The results in (Zhou et al., 2018) show that heating energy saving potential from building envelope in China is 30.9%–66.1%. The reasons for high energy consumption are high indoor temperature and window opening. In addition, a new Energy Management Strategies (EMS) for hybrid electric vehicle (HEV) is proposed based on operation-mode prediction using a Markov chain in (Liu et al., 2018). It determines the on-line correction of torque distribution between the engine and the electric motor. In (Phurailatpam et al., 2018) DC micro-grid is considered for a village, residential and commercial building, which is optimization and comparison of results for various micro-grid configurations. In (Zhang et al., 2018) the multi-micro-grid (MMG) operation is formulated as a transaction commitment problem. Authors design a two-stage robust optimization based MMG coordinated operation approach and describe discrete feature of energy interaction behavior among multiple micro-grids. The reference (Moradi et al., 2018) proposes a novel optimization model to minimize operation of micro-grid and maintenance costs as well as emission costs. The reference (Cesena et al., 2018) proposes both dynamic reliability price signals with a stochastic approach and a MILP tool to co-optimize micro-grid behavior when facing conflicting signals. Micro-grid can provide users with energy, storage and reliability services. The reference (Vasak and Kujundžić, 2018) is focused on the problem of controlling the energy storage, where the energy exchange between the storage and the remaining system is performed in the required amount with maximum efficiency. An optimal operation model for a multi-agent system (MAS) based micro-grid is proposed (Zheng et al., 2018). The aim of the model is to save the overall energy cost. In (Mehdizadeh et al., 2018) risk-based short-term generation scheduling of renewable micro-grid is proposed, and the effects of demand response program are investigated. Authors in (Sardou et al., 2018) propose a novel robust model for energy management of micro-grid and propose a validation method based on Monte Carlo simulation combining the advantages of both classic and heuristic methods. Reference (Netto et al., 2018) presents a framework to analyze the problem of real-time management of smart grids. Authors in (Ghasemi and Enayatzare, 2018) study the energy management of an isolated rural micro-grid and propose a new stochastic optimization framework to keep generation and demand in balance. In (Nunna and Doolla, 2013) an agent-based energy-management system is proposed to facilitate power trading among micro-grids with demand response. An index-based incentive mechanism is also proposed to encourage customers participating in DR based on frequency and size of the participation. A secondary demand response program is proposed in (Rezaei and Kalantar, 2015) to contribute into the micro-grid frequency aware energy management system. The reference (Liu et al., 2017) proposes a distributed energy management method for interconnected operations of combined heat and power (CHP)-based micro-grids with demand response (DR). Reference (Tabar et al., 2017) introduces portable renewable energy resource (PRER) and considers effect of them, where the multi objective and stochastic management are considered with various loads and sources.

Through the analysis of the existing research, it is obvious that smart micro-grid plays an important role in distributed power generation and power grid dispatching. At the same time, with the development of smart grid technology and the progress of demand-side management technology, demand response, as an important energy management method, is one of the key factors in micro-grid dispatching. Existing research has done a lot of research on the basic structure and conventional dispatching strategy of micro-grid, and some of them have also analyzed the basic principles and application methods of demand response in depth. However, under the background of large-scale application of intelligent power utilization technology and intelligent terminal technology, the role of DR program in micro-grid dispatching will become more and more obvious. Renewable energy generation, load and demand response module are important components of smart micro-grid. The scheduling relationship and influence mechanism between them are the key factors to realize the economic operation of micro-grid. In order to make up for this gap, this paper and studies the energy management of smart micro-grid with response load and distributed power generation considering the DR program. In this paper, a smart micro-grid with distributed generation, load and demand response is constructed, and a mathematical model is established for each energy unit. According to the structural characteristics of the established micro-grid, an operation optimization scheduling strategy is also formulated. Based on the modeling of micro-grid, an operation optimization model suitable for smart micro-grid is established. The optimization model established takes the minimum operating cost of the micro-grid system as the objective function, and takes into account factors such as load reduction, load transfer and demand response electricity price. Based on load constraints, operation constraints, user satisfaction and other constraints, Genetic Algorithm (GA) is used to solve the operation optimization model. Then, a micro-grid project in China is used to carry out optimization simulation to verify the validity and accuracy of the model established in this paper. We simulate the dispatching strategy and load response of the micro-grid under three different demand response strategies, and discuss and analysis the system operation cost and the key factors affecting the cost under different strategies. Finally, Natural Gas (NG) price is used as a sensitive factor to influence the DR effect, and the results show that the fluctuation of NG price has a great influence on the DR effect.

The main contributions of this paper are as follows:

• Presenting an optimization model based on Demand Response mechanism for micro-grid energy scheduling with response loads and CCHP as well as PV units and ES.

• Employing the TOU tariff and RTP tariff to implement demand response planning for response loads in micro-grid to realize economical and efficient operation of micro-grid and improve the utilization level of clean energy.

• Considering the operation cost under different electricity price policies as the objective function.

• Using the GA approach for minimizing objective functions and determining the best operation strategy by the clean distributed generation resources and demand response program.