Keywords

1 Introduction

As a result of the fossil-fuels backed industrialization within the last two centuries, the inevitable greenhouse effect of the exhaust gases—mainly CO2, has made the single biggest impact on the climate change. According to publicly available ice core data provided by Etheridge et al. (1996) visualized in Fig. 1, CO2 mixing levels were between 270 and 275 ppm ranges during pre-industrialization era while it is stated that it is over 400 ppm according most recent studies.

Fig. 1
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Ice cap data showing CO2 mixing levelsFootnote

Source CDIAC. Carbon Dioxide Information Analysis Center. Historical CO2 Records from the Law Dome. DE08, DE08 2, and DSS Ice Cores. Available online: https://cdiac.ess-dive.lbl.gov/trends/co2/lawdome.html. (Accessed on 10 October 2020).

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Today, scientists have arrived at a consensus that the temperature anomalies are the result of human activities (Cook et al., 2016). In Fig. 2 the temperature data taken from Morice et al. (2012)’s the HadCRUT4 datasetFootnote 2 on global climate change it can be clearly seen that the anomaly has been accelerated alarmingly in the last decades.

Fig. 2
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Temperature anomalies within the last century

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The world leadership has arrived to the point that deepening climate crisis deserves the top priority. Thus in 2015 in Paris there have been arrived at the first legally binding international agreement concerning climate change. The agreement aims to limit temperature increase well below 2 °C degrees above pre-industrialization levels in this century.

Albeit the situation is worsening continuously, the necessity of employing conventional power generating systems has still been considered as a valid option according to certain policy makers because of the growing energy demand. However, the green-house effect is well beyond the tolerable levels according to the consensus of the most prominent researchers of the world and there’s no sustainable way for fossil based energy being an important part of the power grids throughout world in the future.

Today, renewable energy systems are on an accelerated rise in the overall power production. According to REN21,Footnote 3 a think-tank focusing on renewable energy, on its “2019 Global Status Report” it has been stated that, renewables correspond to %26 of all produced electrical energy as of 2018 end by doubling their corporate resourcing compared to previous year. The biggest penetration to the grid comes mainly from hydro, wind power and solar systems. As the advances in technology of solar panels are being witnessed, in particular pure solar systems or hybrid systems coupled with an array of other elements besides solar, like wind turbines are widely accepted as the future of renewable power production.

Because of the nature of the resources of renewable energy generation, most promising systems tend to be the ones aiming local demand. These decentralized systems with considerably small capacity are considered as microgrids. Because of the volatility of renewable energy resources, a system depending on a single source cannot be considered as stable. Thus microgrids are systems which usually consist of several subsystems harvesting energy from various renewable energy resources simultaneously as well as employing conventional power generating systems (gas/diesel generators or even utility grid) in case of need. Moreover these systems also bear the ability to store the excess power by utilizing batteries and battery management systems.

Considering the multi-component structure of these systems along with the adherent complexity, reducing power production costs and controlling emission values below target levels need system optimization. In this study, a systematic review of papers on energy management systems and smart energy management systems which aim to improve energy management by experimenting with various computational optimization techniques has been conducted. Selection criteria and other details are explained in Sect. 3.

2 Renewable Energy and Applications

Renewable energy or sometimes referred as clean energy is the type of energy derived from natural resources which are capable of being naturally replenished without any human efforts. The fact that it is being naturally replenished is a total outcome of natural events like the sun light, wind, potential or kinetic energy of water masses or biomass hence the term renewable has been coined for this type of energy sources.

Less than two centuries ago, almost all of the energy consumption of the world population was coming from wood which can be technically considered as the most naive form of renewable energy source, although not a carbon–neutral one. After the industrial revolution, from the mid nineteenth century, coal and later petroleum started to replace this primitive source of energy and nearly a hundred years later, around mid twentieth century, with the addition of natural gas almost all of the energy consumption was coming from fossil fuels. The most recent data derived by US Energy Information Administration clearly shows that other than hydroelectric, renewable sources have barely entered to grid shortly before 2000s.Footnote 4 The same data shows a fascinating trend in non-hydro resources that within two decades they have managed to climb to a total power generating supply level in the whole US grid which multiplying hydro recourses and nearly as much as nuclear sources. However, the dominant source of energy is still fossil fuels which should be changed in the favor of carbon–neutral energy sources immediately.

2.1 Hydroelectric Power

Hydroelectric power is generated from the fast-running or falling water like rivers. Since ancient times, humans tried to explore the potential of water in generating the necessary motion for their watermills for processing grains, for watering the crop lands or for different necessities like cutting timber. During centuries, besides these traditional use of hydro power naturally evolved into ideas for generating electricity.

In modern hydro power generating facilities, a running water or falling water from a reservoir is directed to turbines by using tubes and hence generating electricity by propelling the turbine blades which are internally connected to generators.

2.2 Solar Power

Solar energy is the type of energy derived from the sun itself. The sun is the main source of energy of the life on the Earth and it has been used in primitive forms throughout the human history for different purposes like heating water or drying foods. Today solar power means mostly generating electricity by photovoltaics (PV) or concentrated solar power (CSP).

In PV sunlight is turned int o electricity using solar panels which exploit photovoltaic effect, a chemical phenomenon. In CSP sunlight is concentrated to a small area in order to heat water and produce electricity via running a steam turbine. These two approaches can be used in an hybrid way for producing both electricity and heat, although in today’s renewable energy vision especially PV has a very bright future as the solar panel technology advances as nearly efficient as harvesting nearly %50 of solar energy it takes.

2.3 Wind Power

Sunlight causes, different parts of lands and oceans, to have different temperature, pressure and humility profiles. As a result of this divergence between these different regions of Earth, they warm up and cool down differently compared to each other hence the air currents between these regions occur. As the warm air elevates in the atmosphere another cooler mass of air fills in to the region. This movement of mass of air is called what basically known as winds.

Wind power is usually generated by the help of wind turbines which are basically blades connected to rotor of a generator, similar to the principles of harnessing kinetical energy of water in hydroelectrical units.

2.4 Other Renewables and Contemporary Trends

Among others renewable energy sources, tidal (generating electricity from ocean waves’ kinetic energy), biomass (generating electricity from burning gases which arise as the result of the biochemical decomposition of organic material or simply burning the solid waste and using a steam generator) and geothermal (generating electricity or heating an area by using naturally available geothermal water resources) can be listed as the most promising technologies.

However, the main trend in RES is using solar energy based systems especially PVs with solar panels having the most recent advanced infrastructures in order to harness maximum energy from the direct sunlight in addition to the wind turbines. These two type of RES seem to be prevailing as the main generating components in most of the MG architectures in near future.

3 Microgrids and Smart Energy Management Systems (SEMS)

MGs are systems in which various electricity generation technologies are operated in harmony in order to meet a relatively small electrical energy demand. In line with the needs and constraints, MGs can operate as connected to the main utility grid (UG) or can operate as islanded mode. The aim of micro grids connected to the utility grid is to reduce production costs by utilizing renewable energy resources. On the other hand, in islanded MGs, in addition to reducing consumption costs, it can be an alternative solution if the UG cannot be reached. In micro grid systems, in addition to renewable energy sources such as PV, WT and HEG, traditional components such as DG, MT and FC can be included in the system to secure the electricity distribution. A representative MG is illustrated in the figure below.

As is seen in Fig. 3, energy storage systems can also be included in MGs. Thus, electricity that can be produced at low cost for a certain period of time is stored and it can be used for time periods where production is more costly. At this stage, the issue of the distribution of electricity produced by various sources and technologies within MGs gains great importance. The importance of this issue arises from the consequences of the operation of the system in terms of both economic and environmental costs. As a matter of fact, if the generated energy can be stored, there are constraints and costs of storing energy, on the other hand, if the ESS is included in the system, the lowest cost electricity that can be produced or supplied for a certain period of time (for example via UG) should be preferred to meet the demand. At this point, the problem of determining the most appropriate decisions regarding the operational and distribution issues of the electricity generated by MGs is encountered. In order to overcome this problem, smart energy management systems (SEMS) have come to the fore recently. In the next section SEMS are introduced and related issues are discussed.

Fig. 3
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Microgrid power generating system

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3.1 Smart Energy Management Systems

EMSs are systems in which energy production and distribution are optimized through hardware and software components in order to meet economic and environmental requirements by controlling and monitoring the system. The rapid development in computer hardware and software technologies in recent years has begun to show its effect in this area as well. Especially the advancement of computational optimization techniques such as machine learning and evolutionary algorithms has led to significant improvements in “smart” decision support systems. In short, employing advanced computational optimization techniques is the key factor which turns an EMS into a SEMS.

SEMS applications in the literature show that computational optimization techniques can be applied for different purposes. For instance, in some studies in the literature (see Saiprasad et al., 2019; Nurunnabi et al., 2019; Boqtob et al., 2019) primarily the size and capacities of the whole MG or its various components are optimized in line with needs and constraints. On the other hand, energy production and distribution can be optimized for economic and environmental purposes through EMS of an already installed MG. In this study, such researches are included in the scope of the study. In order to ensure that the performance of the findings obtained as a result of optimizing energy production and distribution through computational optimization techniques on SEMS is testable, the improvement made is desired to be measurable. For this purpose, studies involving comparisons with the performance of the system before optimization or with other optimization techniques are included in the scope of the review. There are many more studies in the literature optimizing SEMS than included in this paper but since studies involving comparisons with the performance of the system before proposed optimization technique is applied, are desired, others are excluded.

The review is summarized in the Tables 1 and 2 below. The Tables contains seven columns for summary information of reviewed studies which are authors and year, power generating components, optimization methods, total reduced cost (%), reference point for cost, reduced emission (%) and reference point for emission, respectively. In the power generating components column, power generating components of the MG system for each study is listed. Computational optimization techniques adopted to optimize energy generation and distribution in SEMS is included in optimization methods column. The fourth and sixth columns include the percentage reduced energy production cost and percentage reduced emission after the optimization techniques are applied in the SEMS.

Table 1 Review summary (reference point (cost): base case)
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Table 2 Review summary (reference point (cost): other techniques)
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As is seen in the Tables 1 and 2, huge improvements are made in terms of energy and emission cost reduction in some studies. Several details should be taken into account in order to avoid the misinterpretation of the information summarized in Tables. Reference point columns for “Total Reduced cost (%)” and “Reduced emission (%)” are added to prevent misinterpretation here since reference points for improvements in SEMS are different. It also would not be wise to compare performance of optimization techniques between studies since each SEMS which is utilized to conduct study has different conditions, components, environmental factors etc. from each other.

Reviewed studies here are divided into two categories in terms of reference point of total cost reduction. The first category include studies, where authors try to compare the concerning technique’s improvements over a base-case scenario, i.e. the case with no computational optimization techniques are employed. The second category consists of the studies where the researchers follow a strategy based on comparing a proposed technique with other techniques. These categories and the related information are summarized in Tables 1 and 2 respectively.

In order to evaluate the information summarized in the Tables properly, which components are included in SEMS should be considered. For instance, some of the studies in Tables 1 and 2 include BES in microgrids. Energy storage systems provide more room for optimization of energy production costs and distribution stability. In Table 1, studies including BES in microgrid starts with (Fazlhashemi et al., 2020) and they used HBB-BC algorithm to optimize the operation cost, real power loss, the voltage stability index (VSI), and the greenhouse gas emissions of the MG. (Elkadeem et al., 2020) proposes a systematic and integrative decision-making approach for efficient planning and assessment of hybrid renewable energy-based MG. (Imran et al., 2020) proposes a heuristic-based programmable energy management controller to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize user comfort and reduce the peak-to-average ratio. Muqeet and Ahmad (2020), considering a real-time university campus that is currently feeding its load from the national grid only, propose an EMS strategy for an institutional MG to reduce its operational cost and increase its self-consumption from green RES. Saberi et al. (2019) proposed a multi-objective model for reducing carbon emission and operation cost in the presence of real time demand response program. Boulal et al. (2018) aims the problem of optimal management of the energy flows within a multi-source system of electricity production. Et-Taoussi et al. (2019) optimize an EMS is based on 24 h ahead forecast data of the residential loads consumption, the photovoltaic power and the electricity tariffs for minimizing the production cost of the active/reactive power, and reducing the CO2 equivalent emissions. Pooranian et al. (2018), address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences.

Remaining studies in Table 1 are the ones which has no BES included in microgrid system, such as (Maulik & Das, 2019) which introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC–DC hybrid MG under load and generation uncertainties. Ansari et al. (2019) proposed a framework that minimizes operation cost, and GHG emissions. Jin et al. (2019) presents a multi-objective energy management to optimize the renewable MG operation while satisfying a demand response and various operation constraints.

On the other hand, there are such studies where several computational optimization techniques are compared in terms of total reduced cost and reduced emission and such studies are listed in Table 2. In some of the studies listed here, several computational optimization techniques are compared with proposed techniques but in order to keep it brief, only best technique compared to proposed technique is displayed.

Number of studies including BES in Table 2 is also dominating same as Table 1. Here, (Elsakaan et al., 2019) aims at finding the optimal scheduling of renewable energy resources in isolated and grid-connected MGs and the problem is formulated as a non-linear constrained multi-objective optimization problem. Murty and Kumar (2020) established an optimal energy dispatch strategy for grid connected and standalone MG. Dey and Bhattacharyya (2019) uses three soft computing techniques which are particle swarm optimization, differential evolution, and differential evolution with local global neighborhoods to perform a novel dynamic cost analysis of a MG and minimize its overall cost which includes fuel, emission, operation and maintenance cost, installation, and depreciation costs. Zhou et al (2019) designed a novel model that has predictive control with feedback correction to optimize the energy dispatch and minimize the operation costs of a MG. Ghanbari-Mobarakeh and Moradian (2019), based on demand response program, proposed a new energy management scheme in order to obtain the optimized performance of the MG. Sedighizadeh et al. (2019) the stochastic operation scheduling of a MG consisting of non-dispatchable and dispatchable resources to minimize operation cost and emissions. Roy (2019) presents a hybrid technique for optimal power flow management and production cost minimization of MGs‐connected system with RES. resources. Trivedi et al. (2018) schedule the generating units within their bounds together with minimizing the fuel cost and emission Values with Interior Search Algorithm (ISA). Hosseini et al. (2017) proposes a sustainable simulation method for managing energy resources from the point of view of virtual power players operating in a smart grid. Kamboj and Chanana (2016) utilized MINLP to obtain the optimal power schedule of a day. Bhoye et al. (2016) solve the problem of Combined Economic Emission Dispatch and it is optimized by meta-heuristic techniques. Aghajani et al. (2015) proposed a multi-objective energy management system in order to optimize MG performance in a short-term in the presence of RESs. Rezvani et al. (2015) proposes a multi-objective framework for the optimal scheduling of a MG in order to concurrently minimize the total operation cost and minimize the emission caused by generating units. In the work of (Vasanthakumar et al., 2015), using CSA, generation cost and emission cost of the MG are minimized while satisfying system hourly demand and system constraints. Roy and Mandal (2014) takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction with ABC algorithm.

Remaining studies in Table 2 are the ones that BES is not included in the microgrid. Alomoush (2019) deals with the multi-objective economic-emission dispatch problem which aims at operating costs, emission level, emission tax, and cost of power purchase from the main external grid. Elattar (2018) adopted modified harmony search (MHS) algorithm to solve the combined economic emission dispatch (CEED) problem of the MG taking into account the solar and wind power cost functions. Jiajie et al. (2015), aiming at minimizing MG total operation costs, set up a mathematical model of MG dynamic economic dispatch based on chance constrained programming. Trivedi et al. (2015) proposes multiple environment dispatch problem solution in MGs using ACO technique to solve the generation dispatch problem.

4 Conclusion

Under the light of contemporary literature on renewable energy, it appears that the main goal in the future of energy markets would be the rapid transformation of power generation into a carbon–neutral state in a very agile manner. This can be achieved only by effectively employing renewable energy resources which is also the consensus of the major stakeholders.

Electricity generation units, which are combination of power generating and storing components that harness renewable energy resources effectively are often qualified as MGs. MGs have the ability to meet the local demand in place, without resorting to the main grid, hence they ease the objective of abandoning fossil fuels and reducing carbon footprint of generated energy per unit. Satisfying local demand without creating any additional burden on the main grid and incurring minimal power losses are also the driving factors that would keep the MGs architecture favorable in the future. However, as today’s RES technologies have not yet reached sufficient capacity, they’re often connected to the main grids.

Another handicap is that these energy sources can show serious fluctuations in electricity production as they are affected by unpredictable or very difficult to predict natural factors like weather or wind characteristics. When also the variability in the electricity demand of consumers, especially for household/private consumption, is considered, energy management i.e. power generation and dispatch in MGs can become quite complex. In this respect, it is beneficial to use prediction techniques along with the optimization of EMS in order to reduce the uncertainty in natural conditions.

When the trend in electricity markets is obviously pro-liberal and as long as the auction based pricing schemes are valid, demand response programs are fundamental for managing the load on the grid. Moreover, grid participants need to plan their operations and dispatch schemes according to their costs and market prices. These costs are broadly power generating costs and environmental costs. Although the cost of generating electricity has been studied well and always considered as the main objective so far, environmental costs are no-more a secondary concern but a primary one and should has to be managed very carefully along with operating costs. In this sense, it seems beneficial to adopt a broader approach that would put demand response programs in the center of EMS optimization in order to minimize both the economic and environmental costs of fluctuations in electrical load.