 Original research
 Open Access
Optimal energy management for industrial microgrids with highpenetration renewables
 Han Li^{1},
 Abinet Tesfaye Eseye^{2, 3}Email authorView ORCID ID profile,
 Jianhua Zhang^{2} and
 Dehua Zheng^{3}
https://doi.org/10.1186/s4160101700406
© The Author(s) 2017
Received: 7 December 2016
Accepted: 28 February 2017
Published: 8 April 2017
Abstract
This paper presents a dayahead optimal energy management strategy for economic operation of industrial microgrids with highpenetration renewables under both isolated and gridconnected operation modes. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a dayahead scheduling horizon with one hour time step, taking into account forecasted renewable energy generations and electrical load demands. Besides satisfying its local energy demands, the microgrid considered in this paper (a real industrial microgrid, “Goldwind Smart Microgrid System” in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from the main grid. Performance objectives include minimization of fuel cost, operation and maintenance costs and energy purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based reference energy management approach and confirmed that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.
Keywords
 Energy management
 Genetic algorithm
 Microgrid
 Regrouping particle swarm optimization
 Renewable energy
1 Introduction
Microgrids are a group of interconnected loads, distributed energy resources (including conventional energy sources and renewables) and energy storage systems at a distribution level with distinct electrical boundaries. A microgrid has black start capability and can operate either in isolated or nonisolated mode in connection with other microgrids or main electricity grid.
Nonisolated (gridconnected) microgrids can either send (sell) power to the main grid or receive (buy) from the main grid. This electric power trading with the main grid has traditionally been based on a fixed, predetermined price per kWh. However, with the incorporation of smart meter technologies, capable of accurately measuring energy production and consumption in each time instant, a shift to timevarying electricity pricing models is being occurred recently [1]. Advanced control technologies that can combine together several generation systems and energy storage systems in microgrid entity are emerging to offer customers the opportunity to access reliable and secured electricity locally, sell power during surplus generation or peak grid price time periods, and buy power in case of generation scarcity or cheap electricity prices time instants.
This energy exchange strategy development motivates microgrid operators to adapt their energy trading actions with the main grid and/or other microgrids according to the current electricity price and trading conditions in order to minimize energy production running cost (fuel cost), ensure maximum utilization of renewables, maximize economic benefits of the energy storage systems. To achieve this, specific energy management system should have to be put in place [1–4].
The topic of optimization (cost minimization or profit maximization) in microgrids through energy management has already been dealt with by several researchers in different contexts.
An energy management model, with sensitivity analysis for energy storage capacity investment and electricity load demand growth, for searching optimum operating policies for maximization of profit in a microgrid system in Taiwan is presented in [5]. In [6], the minimization of total costs for energy production and transportation of two interconnected microgrids that can trade electric energy with each other but not connected to the main power grid is addressed. For this aim, a distributed and a central control strategy are examined using an iterative approach and an analytical convex optimization method.
Concerning the issue of energy exchange of a microgrid with the main power grid, [7] targets on the development of a neural network based energy management system (EMS) to allocate the dispatch of generation sources in a microgrid to take part in the energy trading market and minimize global energy production costs. Reference [8] introduces an energy control apparatus called “Energy Box” for controlling residential home or small business electrical energy utilization in an environment of demand sensitive realtime electricity pricing. A stochastic dynamic programming method is employed based on forecast information from load demands, weather, and grid price for optimally managing the utilization, storage and selling/buying of electrical energy. Reference [9] suggests an optimization model based on hierarchical control for a microgrid configuration capable of participating to the wholesale energy trading market as both energy consumer and producer with the objective of minimizing energy production costs and maximizing energy trading revenues.
Reference [10] proposes a generic mixed integer linear programming technique for operating cost minimization in marketbased price environments for a residential microgrid including electrical and thermal loads, energy storage units and some controllable loads. Reference [11] presents an online optimal energy/power control strategy for the operation of energy storage in gridconnected microgrids. The approach is based on a mixedintegerlinearprogramming formulated over a rolling horizon window, considering predicted future electricity load demands and renewable energy generations.
Reference [12] presents a genetic algorithm (GA) for optimal unit sizing of an isolated microgrid considering multiple objectives including lifecycle cost minimization, renewable energy penetration maximization, and emission reduction. In [13, 14], particle swarm optimization (PSO) has been applied for realtime energy management of standalone microgrids.
In most of the literatures reported above, regarding energy management strategies in microgrids, a single energy storage unit is considered. The integration and combined optimal storage management of microgrids containing two or more energy storage units (ESUs) have not been considered so far. Moreover, the PSO is seen to suffer from stagnation once particles have prematurely converged to any particular region of the search space in the energy management strategies that have applied the standard version of PSO for solving the energy management optimization problem [15].
In this paper, we propose a RegPSO approach to optimally solve the EMS optimization model. To evaluate and compare the performances of this approach, another modern optimization method, genetic algorithm (GA) was also implemented.
The rest of the paper is organized as follows. Section II discusses the formulations of the objective and constraint functions. In Section III, the proposed method of optimal energy management strategy and the RegPSO algorithm are presented. The case study simulation results are discussed and performance comparisons are provided in Section IV, and finally the paper is concluded in Section V.
2 Discussion
2.1 Microgrid energy management optimization model
The objective problem and constraint functions of the optimization model for energy management in the microgrid considering the two possible operation modes are formulated in this section. In the isolated mode, the microgrid objective is formulated to minimize the energy production cost (fuel cost), and the operation and maintenance costs within the microgrid. While operating in gridconnected mode, the microgrid can either send (sell) power to the main grid or receive (buy) from the main grid. During the periods receiving power from the main grid, the microgrid is expected to minimize the energy production cost, operation and maintenance cost and the expense of buying power from the main grid; while sending power to the main grid, the objective is to maximize the profit which is the energy selling revenue minus the energy production cost, and operation and maintenance cost.
This objective function is subjected to six decision variables: the charging/discharging power of the VRB, state of charge (SOC) of the VRB, charging/discharging power of the LiIon battery, SOC of the LiIon battery, the diesel generator power output, and the quantity of power exchange with the main grid.
2.2 Formulation of objective functions

24hahead hourly load demand forecast

24hahead hourly wind power forecast

24hahead hourly PV power forecast

Grid price forecast, or prespecified grid price
The objective functions are formulated independently by considering three operational cases based on the microgrid operating mode and the power flow directions between the microgrid and the main grid. In case I, the objective function for the isolated mode of operation is considered. In case II, the microgrid is in gridconnected mode and it receives (buys) power from the main grid. While in case III, the microgrid is also in gridconnected mode but it sends (sells) power to the main grid.
2.2.1 Case I – isolated mode
In case I, the objective targets to minimize the energy production cost (fuel cost), and the operation and maintenance costs within the microgrid.
Where, a_{i}, b_{i} and c_{i} are the cost function parameters.
τ _{ i }(t) = 1, if the i^{th} dispatchable DG is in operation;
τ _{ i }(t) = 0, if the i^{th} dispatchable DG is OFF at time t;
otherwise
Where, sc _{ i } is the start up cost of dispatchable DG i.
c _{ OM,i }(t) is the operation and maintenance cost of the i^{th} dispatchable DG at time t; c _{ OMwind }(t) is the operation and maintenance cost of the wind power generation system at time t; P _{ wind }(t) is the forecasted wind generation at time t; c _{ OMpv }(t) is the operation and maintenance cost of the PV system at time t; P _{ pv }(t) is the forecasted PV generation at time t; C _{ OMes,j }(t) is the operation and maintenance cost of the j^{th} energy storage unit at time t; P _{ es,j }(t) is the j^{th} energy storage charging/discharging power at time t.
2.2.2 Case II – Nonisolated mode  buying power from main grid
In this case, the objective aims in minimizing the energy production cost, the operation and maintenance costs and the expenses of energy purchasing from the main grid.
Where, c _{ gridbuy }(t) is the electricity buying price from the main grid at time t; P _{ grid }(t) is the power purchased from the main grid at time t, P _{ grid }(t) > 0.
2.2.3 Case III  Nonisolated mode  selling power to main grid
Here, the objective aims in maximizing the profit which is the energy selling revenue minus the energy production cost and the operation and maintenance costs within the microgrid.
Where, c _{ gridsell }(t) is the electricity selling price to the main grid at time t; P _{ grid }(t) is the power sold to the main grid at time t, P _{ grid }(t) < 0.
2.3 Formulation of constraint functions
The objective functions formulated above are subjected to the following constraints; comprising ESS units’ capacity and operational limits, dispatchable DGs’ power limit, grid power transfer limits, and all other technical requirements in the microgrid:
2.3.1 Power output of the i^{th} dispatchable DG at time t
2.3.2 Grid power exchange limits
The grid power exchange minimum (\( {P}_{grid}^{\min }(t) \)) and maximum (\( {P}_{grid}^{\max }(t) \)) limits can be set as a large amount or the capacity of the transformer linking the microgrid and the main grid.
2.3.3 Demandsupply balance
where P _{ load }(t) denotes the forecasted load demands at time t.
2.3.4 ESS units charging/discharging power limits
P _{ es,j }(t) > 0, the i^{th} energy storage is discharging;
P _{ es,j }(t) < 0, the i^{th} energy storage is charging;
P _{ es,j }(t) = 0, the i^{th} energy storage is inactive.
2.3.5 ESS units dynamic operation performance
Where, η _{ es,j }(t) is the i^{th} energy storage unit charging or discharging efficiency at time t; C _{ es,j } denotes the rated storage capacity of j^{th} energy storage unit.
Thus, the decision variables that need to be determined are the ESUs’ charging/discharging power P _{ es,j }(t) and their state of charges SOC _{ es,j }(t) (for i =1, 2, …, q); the power output of dispatchable DGs P _{ i }(t),and the quantity of power exchange with the main grid P _{ grid }(t) at time t.
3 Method
3.1 Proposed microgrid energy management strategy
The EMS in this study is restricted to control only the real power. Power quality, frequency regulation, and voltage stability are supposed to be controlled at the generation level. Microgrid black start operation or synchronization with the main grid is not considered either. The proposed system comprises functions, such as an energy storage units charging/discharging power economic scheduling, diesel generator output power optimal scheduling, forecasting for renewable generators and load demands, and energy trading participation with the main grid.
3.2 The RegPSO algorithm
PSO has few variables to update and is simple to implement. Many researches and applications have been successfully implemented using the PSO concept. Reference [18] presents a general idea of PSO and its applications in power systems, and also gives comparisons with other optimization methods.
where \( {x}_j^L \) and \( {x}_j^U \) are, respectively, the lower and upper bounds of the design search space along dimension j for j = 1, 2, …, n.
where ω_{max} and ω_{min} are the initial and final inertia weight values, respectively, k_{max} is the maximum number of iterations used; c_{1} and c_{2} are the cognitive and social learning rates respectively, and r_{1} and r_{2} are random numbers in the range of 0 and 1. The parameters c_{1} and c_{2} represent the relative importance of the position (memory) of the particle itself to the position (memory) of the swarm; pBest or P _{ best,i } is the best position achieved so for by particle i, while gBest or G _{ best } is the global best position of all the particles in the swarm.
where. represents the Euclidean norm.
where ε, called the stagnation threshold.
Where, λ is the velocity clamping factor.
4 Result
4.1 Test case
The operation and maintenance costs considered within the microgrid are, respectively 0.3767 c$/kWh, 0.2169 c$/kWh, 0.5767 c$/kWh, 0.003 c$/kWh and 0.0015 c$/kWh for the wind turbine system, PV systems, diesel generator, VRB and LiIon battery.
4.2 Operation in isolated mode (case I)
As shown in the figures above, during the first 4 h [12 am – 4 am) of the simulation period, there is a significant generation of wind energy and no generation from the PV source. In this period, the renewable energy completely supplies the load demands and charges the ESUs which were at minimum SOCs (20%) before the simulation started, and the DE is off (zero power) to reduce the fuel cost as there is enough renewable generation in the microgrid. The ESUs continuously charge and their SOCs increases until 4 am, shown in Fig. 9. However, although they don't get fully charged the ESUs stop charging and their charging powers come to zero (inactive state) at 4 am since the available renewable generation can only supply the load demand since from this time till 1 pm.
During the period [1 pm – 9 pm), the power generation from both the wind and PV sources is not enough to supply the load demands, and thus the ESUs start discharging to send power to the microgrid together with the wind, PV and DE.
The ESUs continuously discharge and reach their minimum storage capacity (240 kWh for VRB and 160 kWh for LiIon battery), shown in Fig. 9, at 9 pm and their discharging power come zero then after. To reduce the cost of energy production, the ESUs are inactive state since then; until they will be charged again by an available excess renewable generations in the microgrid and their SOCs are kept at minimum value of 20%. The wind and DE supply the load demands from 9 pm to 12 am.
4.3 Operation in Nonisolated mode (case II & III)
During the period [12 am – 6 am), shown in Fig. 11, the renewable energy completely supplies the load demands within the industrial park and charges the ESUs which were considered to be at their minimum SOC (20%) before the simulation started at zero time (12 am). Moreover in this period, the microgrid sells the surplus generation to the main grid.
The ESUs continuously charge and reach their maximum storage capacity, shown in Fig. 12, at 6 am and then their charging power become zero. During the period [6 am – 1 pm), there is still an excess generation in the microgrid, however the ESUs are already fully charged, thus the microgrid keeps selling the excess energy to the main grid.
The power generation from the renewables is not enough to supply the load demands and the grid price is peak (expensive) during the period [1 pm – 3 pm). Hence, the ESUs start discharging to support the microgrid load demands together with the wind and PV and the DE and grid powers are zero in this period to minimize the total cost as shown Fig. 11.
During the period [3 pm – 6 pm), the generation from the renewables is insufficient to supply the microgrid load demands and the grid price is moderate. Hence, the ESUs stop discharging for later peak hour demand use and the microgrid utilizes the generations from the DE and main grid for economic reasons as shown Fig. 11.
In the period [6 pm – 9 pm), the microgrid load demand is greater than the local generations from the renewables. Since the electricity buying price is expensive in this period, the ESUs restart discharging to supply the load together with the wind and DE. The ESUs continuously discharge and reach their minimum storage capacity (240 kWh for VRB and 160 kWh for LiIon battery), shown in Fig. 12, at 9 pm and their discharging power come zero then after. Thus, the ESUs are in inactive state since then; until they will be charged again by an available excess renewable generations in the microgrid and their SOCs are kept at minimum value of 20% as shown in Fig. 12.
The load demand is supplied by the wind and DE during the period [9 pm – 11 pm) and the grid power is zero for minimum cost. In the period [11 pm – 12 am), there comes again excess renewable generation from the wind source and the microgrid sells this energy to the main grid instead of starting charging the ESU for maximum daily total profit.
Fuel and energy trading costs by RegPSO and GA
Optim. Algorithm  Daily Total Cost ($)  

Daily Energy Production Fuel Cost  Daily Grid Power Purchasing Expense  Daily Energy Selling Profit  
Isolated Mode  Nonisolated Mode  Isolated Mode  Nonisolated Mode  Isolated Mode  Nonisolated Mode  
RegPSO  354.42  247.04  0  38.654  0  211.66 
GA  383.53  257  0  42.862  0  211.66 
As seen in Fig. 18, during the period [12 am – 1 pm), the microgrid sells energy to the main grid and gets profit. After 1 pm, except at [11 pm – 12 am), the microgrid has no surplus generation to sell, and hence the selling income is zero. Moreover, since the electricity selling price to the main grid is fixed throughout the day, the hourly selling income values obtained by both algorithms (RegPSO and GA) are almost the same.
Computation time for RegPSO and GA
Optimization Algorithm  Total Computation Time (seconds)  

Isolated Mode  Nonisolated Mode  
RegPSO  1.8678  2.1152 
GA  14.9845  16.3456 
5 Conclusion
Optimal dynamic energy scheduling strategy for a WindPVDEVRBLiIon industrial microgrid under both isolated and gridtied operation modes was proposed in this study using the RegPSO algorithm. The proposed approach takes into account the fluctuations of renewables and load demands in the microgrid and appropriate dayahead forecasts have been made to overcome these fluctuations. Simulation results have demonstrated the effectiveness and possible advantages of the developed energy management strategy in minimizing the energy production fuel cost, grid power purchasing expense, maximizing the energy selling profit, maximizing the economic usage of ESUs and enhancing the utilization of the renewables within the microgrid. Comparison of simulation results with GAbased approach, demonstrated the effectiveness of the proposed RegPSObased energy management strategy in resulting a possible reduced energy production fuel cost and grid power purchasing expense for the microgrid. Moreover, the proposed approach is fast convergent and results global optimum solutions in an acceptable short computation time. This also manifests the ability of the proposed approach for real time energy management of microgrids with any number of renewable DGs and ESUs under both operation modes.
Declarations
Authors’ contributions
Han Li contributed to the conception of the study. Abinet Tesfaye Eseye contributed significantly to analysis, manuscript preparation and manuscript submission as a corresponding author; Jianhua Zhang and Dehua Zheng helped perform the study analysis with constructive discussions, senior professional advice and revised the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 Velik, R., & Nicolay, P. (2014). Gridpricedependent energy management in microgrids using a modified simulated annealing tripleoptimizer. Appl Energy, 130, 384–395.View ArticleGoogle Scholar
 Velik, R. (2013). “The influence of battery storage size on photovoltaics energy selfconsumption for gridconnected residential buildings.” IJARER International Journal of Advanced Renewable Energy Research, 2(6).Google Scholar
 Velik, R. (2013). “Battery storage versus neighbourhood energy exchange to maximize local photovoltaics energy consumption in gridconnected residential neighbourhoods.” IJARER International Journal of Advanced Renewable Energy Research, 2(6).Google Scholar
 Velik, R (2013). “Renewable energy selfconsumption versus financial gain maximization strategies in gridconnected residential buildings in a variable grid price scenario.” IJARER International Journal of Advanced Renewable Energy Research, 2(6).Google Scholar
 Chen, Y., Lu, S., Chang, Y., Lee, T., & Hu, M. (2013). Economic analysis and optimal energy management models for microgrid systems: a case study in Taiwan. Appl Energy, 103, 145–154.View ArticleGoogle Scholar
 Gregoratti, D., & Matamoros, J. (2013). Distributed convex optimization of energy flows: the twomicrogrid case. In 1st international black Sea conference on communication and networking (pp. 201–205).Google Scholar
 Celli, G., Pilo, F., Pisano, G., & Soma, G. (2005). Optimal participation of a microgrid to the energy market with an intelligent EMS. In 7th international power engineering conference (pp. 663–668).Google Scholar
 Livengood, D., & Larson, R. (2009). The energy Box: locally automated optimal control of residential electricity usage. Serv Sci, 1(1), 1–16.View ArticleGoogle Scholar
 Mashhour, E., & MoghaddasTafreshi, S. (2010). Integration of distributed energy resources into low voltage grid: A marketbased multiperiod optimization model. Electr Pow Syst Res, 80(4), 473–480.View ArticleGoogle Scholar
 Kriett, P., & Salani, M. (2012). Optimal control of a residential microgrid. Energy, 42(1), 321–330.View ArticleGoogle Scholar
 Malysz, P., Sirouspour, S. and Emadi, A. (2014). “An Optimal Energy Storage Control Strategy for Gridconnected Microgrids.” IEEE Transactions on Smart Grid, 5(4). July.Google Scholar
 Zhao, B., Zhang, X., Li, P., Wang, K., Xue, M., & Wang, C. (2014). Optimal sizing, operating strategy and operational experience of a standalone microgrid on Dongfushan Island. Appl Energy, 113, 1656–1666.View ArticleGoogle Scholar
 Pourmousavi, S. A., Nehrir, M. H., Colson, C. M., and Wang, C. (2010). “Realtime Energy management of a StandAlone Hybrid WindMicroturbine Energy System Using Paricle Swarm Optimization.” IEEE Transactions on Sustainable Energy, 1(3). October.Google Scholar
 Aric James, L. (2013). Realtime energy management of an islanded microgrid using multiobjective particle swarm optimization. Bozeman: Master’s Thesis, Electrical Engineering, Montana State University.Google Scholar
 Evers, G. I., & Ghalia, M. B. (2009). Regrouping particle swarm optimization: a New global optimization algorithm with improved performance consistency across benchmarks. In IEEE international conference on systems, Man and cybernetics (SMC) (pp. 3901–3908).Google Scholar
 Zheng, Z. (2012). Optimal energy management for microgrids. South Calorina: PhD Dissertation Electrical and Computer Engineering, Clemson University, Clemson.Google Scholar
 Borghetti, A., Bosetti, M.,and Grillo, S. (2010). “Shortterm scheduling and control of active distribution systems with high penetration of renewable resources.” IEEE Systems Journal, 4(3). SeptemberGoogle Scholar
 Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J.C., & Harley, R. G. (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput, 12(2), 171–195.View ArticleGoogle Scholar
 Evers, G. (2016). PSO Research Toolbox Documentation (Version: 20160308), M.S. thesis code documentation. http://www.georgeevers.org/pso_research_toolbox_documentation.pdf. Accessed 8 Mar 2016.