# A Review of Energy Management Systems and Organizational Structures of Prosumers

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## Abstract

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## 1. Introduction

- Other approaches [59];
- Hybrid methods (a combination of several methods).

- Optimization frameworks;
- Methods for predicting electricity generation;
- Methods for predicting electricity consumption;
- Participation in the electricity market.

## 2. Methodology

- scientific papers published in the last five years were taken into account, with the exception of highly cited papers with a larger scope published more than five years ago that were also taken into account;
- papers dealing with the development of EMS systems for prosumers and microgrids were taken into account;
- papers dealing with the development of EMS based on ESS, DSM, hybrid EMS and EV were considered;
- review papers on the topics of prosumer EMS, microgrid EMS, input data prediction in optimization problems and the electricity market were taken into account, but also published in the last five years, with the exception of highly cited papers;
- fundamental books of high quality with the topic of RES integration and their impact on the grid were considered;
- other aspects, such as security and communication technologies, were not taken into account.

- a quality and comprehensive overview of the research topic, analysis of review papers published so far, as well as identification of room for improvement and the gap planned to be filled by the current research are presented in the introductory part;
- a detailed overview of the prosumer control structure, EMS with a detailed examination of each aspect and the market environment are presented as a result of the conducted research;
- recommendations, the conclusion and room for improvement are based on a detailed review of scientific papers.

## 3. An Overview of the Prosumer Control Structure

- Primary regulation is realized using a fast local controller in control of only one element of the microgrid, be it DG, a controllable load or several aggregated elements;
- Secondary regulation is usually realized using the central controller in control of coordination and supervision of all local controllers;
- Tertiary regulation serves as an intermediary between the central microgrid controller and external agents such as aggregators, grid operators, or electricity market operators.

- Lower control functions—regulation of voltage, frequency, active and reactive power at the level of local controllers of each controllable element of the microgrid;
- Essential control functions—operation between on-grid and off-grid mode and vice versa, and energy management;
- Upward control functions—realization of communication with the system operator, market operator, and aggregator, and integration into external information and communication systems.

## 4. An Overview of a Prosumer EMS

- hard constraints—must be satisfied in the solution;
- soft constraints—satisfaction in the solution is not essential but desirable.

- a rule-based algorithm—used for shifting loads to periods of low prices and reducing peak load;
- artificial intelligence—used for finding optimum maintenance of heat, consumption energy, renewable energy use, turning devices on and off, reducing total energy costs using an artificial neural network (ANN), fuzzy logic control (FL) and an adaptive neuro-fuzzy inference system (ANFIS);
- optimization methods (techniques)—the objective function is the minimization of errors, cost, optimal design and management using classical mathematical and heuristic optimization methods (techniques).

- evolutionary computing (EC);
- swarm intelligence (SI).

- human machine interface (HMI) of the operator for monitoring and entering input settings;
- supervisory control and data acquisition (SCADA);
- a module for predicting input data required for optimization based on current and historical measurement data;
- the optimization module responsible for optimal operations by generating decisions for the observed scheduling horizon.

- The type of the prosumer and the elements the prosumer integrates;
- The market environment in which the prosumer is integrated;
- Methods for predicting input parameters in optimization problems;
- Optimization frameworks and optimization problems of the prosumer EMS.

- electricity sources:
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- controllable sources (CS),
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- uncontrollable sources (RES);

- electricity loads:
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- controllable loads (CL),
- –
- uncontrollable (critical) loads (UL);

- energy storage systems:
- –
- electrochemical systems (secondary batteries),
- –
- chemical systems,
- –
- electrical systems.

#### 4.1. Types and Elements of the Prosumer

#### 4.1.1. Electricity Sources

- the minimum/maximum output power of the aggregator (electricity power source);
- the rate of change of the output power or ramp up/down;
- the minimum electricity generation time and the minimum interruption time of electricity generation or the minimum up/down time;
- electricity generation (working time) costs are most often divided into fuel and start-up costs.

#### 4.1.2. Electricity Loads

**Table 1.**Review of the scientific literature with respect to types, elements and modeling of the power electronics converter of prosumers.

Ref. | CS | RES | EV/PHEV | CL | ESS | BPEC | CCCEL | CCADE and LCADP |
---|---|---|---|---|---|---|---|---|

[85] | MT | PV | No | - | ECS | No | - | Yes |

[86] | - | PV | Yes | Aggr | CHS | - | - | - |

[26] | - | PV | No | - | ECS | No | - | Yes |

[32] | - | PV, WT | No | Tshift | ECS | No | - | Yes |

[37] | GWICE | PV, WT | No | Aggr | ECS | No | - | Yes |

[52] | - | PV | No | - | ECS | No | - | Yes |

[87] | - | PV | No | - | ECS | No | - | Yes |

[88] | CP | - | No | - | TS | - | - | - |

[89] | - | PV, WT | No | - | ECS | No | - | Yes |

[90] | - | PV, WT | No | - | ECS | No | - | Yes |

[91] | - | PV | No | - | ECS | - | Yes | Yes |

[92] | MT, CP | PV | No | - | ECS | No | - | Yes |

[30] | - | PV | No | - | ECS | - | Yes | Yes |

[93] | - | PV, WT | No | - | ECS | - | Yes | Yes |

[47] | MT | PV | No | - | ECS | No | - | Yes |

[94] | - | WT | No | - | ECS | No | - | Yes |

[95] | - | PV, WT | No | - | ECS | No | - | Yes |

[96] | GWICE, MT, CP | PV, WT | No | Aggr, Tshift | ECS, CHS | - | - | - |

[97] | MT, CP | PV | No | - | ECS | No | - | Yes |

[98] | - | PV | No | - | ECS | No | - | Yes |

[99] | - | PV | No | - | ECS | - | Yes | Yes |

[100] | CP | PV | No | Aggr | ECS | No | - | Yes |

[101] | GWICE | PV, WT | Yes | Aggr | ECS | No | - | Yes |

[102] | - | - | No | Tshift, SAD | ECS | No | - | Yes |

[103] | MT | PV, WT | No | - | ECS | No | - | Yes |

[104] | - | - | Yes | Tshift, SAD | ECS | No | - | Yes |

[105] | GWICE | PV | No | Aggr | ECS | - | - | - |

[106] | MT | PV | No | - | ECS | - | - | - |

[107] | MT | PV, WT | No | Tshift | ECS | No | - | Yes |

[108] | MT | PV, WT | No | Tshift, SAD | ECS | No | - | Yes |

[109] | MT | PV, WT | No | - | ECS | No | - | Yes |

[110] | MT, CP | PV, WT | No | - | ECS, CHS | No | - | Yes |

[111] | GWICE | PV, WT | No | Tshift | ECS | No | - | Yes |

[112] | MT | WT | No | Tshift | ECS | No | - | Yes |

[113] | - | - | Yes | - | ECS | No | - | Yes |

[114] | - | PV, WT | No | - | ECS | No | - | Yes |

[115] | GWICE, MT | PV, WT | No | - | ECS, CHS | No | - | Yes |

[116] | GWICE, CP | PV, WT | No | - | ECS | - | - | - |

[46] | GWICE, MT | PV | No | Aggr | ECS | No | - | Yes |

[117] | GWICE, MT | PV, WT | No | Aggr | ECS | No | - | Yes |

[118] | GWICE | PV, WT | No | Aggr | ECS | No | - | Yes |

[119] | GWICE, MT | PV, WT | No | Aggr | ECS | No | - | Yes |

[120] | GWICE | PV, WT | No | - | ECS | No | - | Yes |

[121] | GWICE | PV | No | Aggr | ECS | No | - | Yes |

[122] | GWICE, MT | PV, WT | No | Aggr | ECS | No | - | Yes |

[24] | MT | PV, WT | No | Aggr | ECS | No | - | Yes |

[123] | MT, CP | PV, WT | Yes | Aggr | CHS | No | - | Yes |

[124] | MT | - | No | Aggr | - | - | - | - |

[39] | MT | PV, WT | No | Aggr | ECS | No | - | Yes |

[125] | MT, CP | PV, WT | No | SAD | ECS | No | - | Yes |

[25] | CP | PV, WT | No | - | ECS | No | - | Yes |

[126] | CP | PV | No | Aggr | ECS | No | - | Yes |

[127] | CP | - | No | - | TS | No | - | Yes |

[128] | - | PV, WT | No | SAD | ECS | No | - | No (CC/CV) |

[27] | - | PV, WT | No | - | ECS | No | - | Yes |

[129] | - | PV | No | - | ECS, CHS | No | - | Yes |

[49] | - | PV, WT | No | - | ECS | No | - | No (CC/CV) |

[58] | - | PV | Yes | CCEV | - | - | - | - |

[66] | - | PV | Yes | CCEV | - | - | - | - |

[67] | - | PV | Yes | CCEV, Aggr | ECS | No | - | Yes |

[68] | - | - | Yes | CCEV | - | - | - | - |

[69] | - | PV, WT | Yes | - | ECS | No | - | Yes |

[70] | - | PV | Yes | - | ECS | No | - | Yes |

[71] | - | PV | Yes | CCEV | - | - | - | - |

[72] | - | PV | Yes | CCEV | ECS | No | - | Yes |

[73] | - | PV | Yes | - | ECS | No | - | Yes |

[74] | - | PV | Yes | CCEV | ECS | No | - | Yes |

[55] | - | PV, WT | No | - | ECS | No | - | Yes |

#### 4.1.3. Energy Storage Technologies

- energy management;
- energy storage.

- electrical storage (ES)—(i) supercapacitor and (ii) superconducting coil;
- mechanical storage (MS)—(i) pump-accumulation hydropower plant, (ii) compressed air, and (iii) flywheels;
- electrochemical storage (ECS)—(i) secondary batteries and (ii) instantaneous batteries;
- thermochemical storage (TCS)—solar fuel;
- chemical storage (CHS)—fuel cells;
- thermal storage (TS)—(i) low-temperature energy storage and (ii) high-temperature energy tank.

#### 4.2. Prediction of Input Parameters in Prosumer Optimization Problems

- statistical methods;
- physical methods;
- artificial intelligence methods;
- hybrid methods,

- statistical methods;
- artificial intelligence methods;
- hybrid methods,

- very short term (min–h);
- short-term (h–week);
- medium short-term (month–year);
- long-term (over a year).

#### 4.3. Optimization Framework and Optimization Problems of the Prosumer EMS

- optimization framework;
- optimization method;
- objective function and constraints.

#### 4.3.1. Optimization Framework

#### 4.3.2. Optimization Methods

- classical mathematical programming methods;
- methods based on intelligent search of solution space (global optimum approximation methods, metaheuristics);
- rule-based methods;
- multi-agent systems;
- artificial intelligence methods;
- hybrid methods.

- sequential linear programming (SLP) [124];
- sequential quadratic programming (SQP) [110];
- convex mixed-integer second-order cone programming (CMISOCP) [117];
- hybrid methods of using dynamic programming and linear programming (HDPLP) [47];
- transformation of mixed-integer nonlinear programming in semidefinite programming (TMINLPSP) [103];
- other approaches [72].

#### 4.3.3. Objective Functions and Constraints

- a combination of all three objectives, i.e. economic, technical and environmental [46].

## 5. An Overview of the Prosumer Market Environment

## 6. Recommendations for Future Work

- Optimization problems lack detailed models of EVs that encompass different types of energy management during the charging/discharging process and predict their usage patterns.
- EVs, PV systems and ESS are almost always interfaced with power converters that are regularly left out in optimization models.
- For detailed battery models, it is necessary to consider the amount of charging and discharging power, which is not equal in the entire range but depends on various factors and, most notably, on the state of charge of the battery.
- Input data such as RES generation, load, and market prices into optimization models rarely use exact prediction methods.
- Participation of prosumers in new market mechanisms, especially the local market environments, and detailed modeling of DRP must be further developed and improved.
- Optimization frameworks play a very important role in alleviating the uncertainty associated with RES generation, load and market prices that influence the optimality of the solution. High volatility of RES generation and loads demands higher temporal resolution of the optimization time step, especially when participating in emerging electricity markets.

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 8.**Overview of optimization frameworks, optimization methods, objective functions and constrains of optimization problems.

**Table 2.**Review of the scientific literature with respect to the optimization framework, optimization method, optimization approach, objective function and other specificities of the optimization problem.

Ref. | Optimization Framework | Optimization Method | Time Step | Scheduling Horizon | Optimization Objectives | Optimization Approach | Prediction of Input Data |
---|---|---|---|---|---|---|---|

[85] | Online | MIQP | 1 h | 48 h | Technical | Stochastic | Yes |

[86] | Offline | LP | 1 h | 168 h | Economic | Deterministic | No |

[26] | Online | QP | 30 min | 24 h | Technical, Economic | Deterministic | Yes |

[32] | Online | MILP | 15 min | 24 h | Economic | Deterministic | No |

[37] | Offline | WOA | 1 h | 24 h | Economic | Deterministic | No |

[52] | Online | HGAFL | 15 min | 168 h | Economic | Deterministic | No |

[87] | Offline | HGAFL | 15 min | 168 h | Economic | Deterministic | No |

[88] | Online | MILP | 1 h | 20 h | Economic | Deterministic | No |

[89] | Online | QP | 1 h | 24 h | Economic | Deterministic | No |

[90] | Online | QP | 1 h | 24 h | Economic | Deterministic | No |

[91] | Online | MILP | 15 min | 24 h | Economic | Deterministic | Yes |

[92] | Online | LP, NLP | 15 min | 24 h | Environmental, Economic | Deterministic | No |

[30] | Online | MILP, RBA | 1 min, 15 min | 24 h | Economic | Deterministic | Yes |

[93] | Online | MILP | 1 min, 15 min | 24 h | Economic | Deterministic | Yes |

[47] | Offline | HDPLP | 1 h | 24 h | Economic | Deterministic | No |

[94] | Offline | NLP | 1h | 24 h | Economic | Deterministic | No |

[95] | Offline | PSO | 1h | 96 h | Economic | Deterministic | No |

[96] | Offline | MINLP | 1 h | 24 h | Technical, Economic | Deterministic | No |

[97] | Offline | MILP | 1 h | 24 h | Economic | Robust programming | No |

[98] | Online | QP | 30 min | 24 h | Economic | Deterministic | No |

[99] | Offline | DP | 10 min | 24 h | Economic | Deterministic | Yes |

[100] | Online | MILP | 15 min | 6 h | Economic | Deterministic | Yes |

[101] | Online | SDP | 5 min | 24 h | Technical, Economic | Deterministic | Yes |

[102] | Offline | MILP | 15 min | 24 h | Economic | Deterministic | No |

[103] | Offline | MINLP, SDP, TMINLPSP | 1 h | 24 h | Technical, Economic | Deterministic | No |

[104] | Online | MILP | 1 h | 24 h | Economic | Deterministic | Yes |

[105] | - | MILP | 1 h | 24 h | Economic | Deterministic | No |

[106] | - | MILP | 15 min | 24 h | Economic | Deterministic | No |

[107] | Offline | MILP | 1 min, 10 min, 1 h | 24 h | Economic | Deterministic | No |

[108] | Offline | MILP | 1 h | 24 h | Economic | Stochastic | Yes |

[109] | Offline | MILP | 1 h | 24 h | Economic | Stochastic, Robust programming | Yes |

[110] | Offline | SQP | 1 h | 24 h | Economic | Deterministic | No |

[111] | Offline | MILP | 1 h | 24 h | Economic | Deterministic | No |

[112] | Online | MILP, MINLP | 1 h | 24 h | Economic | Robust programming | No |

[113] | Online | MILP | 15 min | 12 h | Economic | Stochastic | No |

[114] | Online | QP, MINLP | 1h | 96 h, 72 h, 48 h, 24 h, 12 h, 6 h | Technical, Economic | Deterministic | Yes |

[115] | Online | MILP | 1 h, 5 min | 24 h | Technical, Economic | Deterministic | No |

[116] | Online | MILP | 30 min | 24 h | Economic | Deterministic | Yes |

[46] | Online | PSO | 1 h, 1 min | 24 h | Economic, Technical, Environmental | Deterministic | No |

[117] | Offline | CMISOCP | 1 h | 24 h | Technical, Economic | Robust programming | No |

[118] | Offline | PSO | 15 min | 24 h | Economic | Deterministic | No |

[119] | Online | MILP, QP | 30 min, 5 min | 24 h | Economic | Deterministic | No |

[120] | Offline | NLP | 1 h | 24 h | Technical, Economic | Stochastic | No |

[121] | Offline | MINLP | 1 h | 24 h | Economic | Deterministic, Stochastic | No |

[122] | Online | MILP, NLP | 5 min | 24 h | Economic | Deterministic | Yes |

[24] | Offline | HGAPO | 1 h | 24 h | Environmental, Economic | Stochastic | Yes |

[123] | Online | MILP | 1 h | 24 h | Economic | Stochastic | No |

[124] | Online | SLP | 1 h | 24 h | Economic | Deterministic | No |

[39] | Offline | MILP | 1 h | 12 h | Economic | Stochastic | Yes |

[125] | Offline | GA | 15 min | 24 h | Environmental, Economic | Deterministic | No |

[25] | Offline | MVPA | 1 h | 24 h | Economic | Deterministic | No |

[126] | Offline | MIQP | 1 h | 24 h | Economic | Deterministic | No |

[127] | Offline | MILP | 5 min | 24 h | Economic | Deterministic | No |

[128] | Online | MILP | 1 h | 24 h | Economic | Deterministic | No |

[27] | Offline | RBA | 1 h | 48 h | Economic | Deterministic | No |

[129] | Offline | LP, MILP, PSO | 1h | 24 h | Economic | Deterministic | No |

[49] | Online | MILP | 1 h | 24 h | Economic | Deterministic | No |

[58] | Online | MILP, RNN | 15 min | 24 h | Economic | Deterministic | No |

[66] | Online | MILP | 15 min, 1 min | 168 h | Tehnical | Deterministic | Yes |

[67] | Offline | MILP, RBA | 15 min | 24 h | Economic | Deterministic | No |

[68] | Offline | MINLP, GRA | 1 h | 24 h | Economic | Deterministic | No |

[69] | Offline | MILP | 15 min | 24 h | Economic | Deterministic | Yes |

[70] | Offline | NLP | - | 24 h | Economic | Deterministic | No |

[71] | Offline | MILP | 1h | 24 h | Economic | Deterministic | No |

[72] | Offline | - | 1 min | 24 h | Economic | Deterministic | No |

[73] | Offline | DP | 1 h | 24 h | Economic | Deterministic | No |

[74] | Offline | MILP | 1 h | 24 h | Economic | Deterministic | No |

[55] | Offline | MAS | 30 min | 24 h | Economic | Deterministic | No |

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## Share and Cite

**MDPI and ACS Style**

Mišljenović, N.; Žnidarec, M.; Knežević, G.; Šljivac, D.; Sumper, A. A Review of Energy Management Systems and Organizational Structures of Prosumers. *Energies* **2023**, *16*, 3179.
https://doi.org/10.3390/en16073179

**AMA Style**

Mišljenović N, Žnidarec M, Knežević G, Šljivac D, Sumper A. A Review of Energy Management Systems and Organizational Structures of Prosumers. *Energies*. 2023; 16(7):3179.
https://doi.org/10.3390/en16073179

**Chicago/Turabian Style**

Mišljenović, Nemanja, Matej Žnidarec, Goran Knežević, Damir Šljivac, and Andreas Sumper. 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers" *Energies* 16, no. 7: 3179.
https://doi.org/10.3390/en16073179