Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.     

Risk-based performance of power-to-gas storage technology integrated with energy hub system regarding downside risk constrained approach

In modern power systems, the reliability of energy supplies is a real challenge for the operators. The emergence of renewable energy resources, along with multi-career users, requires multi-career systems. In this regard, the energy hub (EH) as an integrated system can be used to increase the reliability of the system. The power-to-gas (P2G) and P2G storage are two practical technologies to achieve high efficiency in energy systems. In this paper, the contribution is optimal scheduling of stochastic problem in EH system amalgamated with CHP unit, P2G storage, thermal storage, boiler, wind power, and electrical storage to supply the heat, gas, and power loads by regarding demand response program (DRP). For the electrical loads, the load shifting strategy is considered to minimize the operational cost of the EH system. In order to manage related uncertainties about electricity price, wind power, and electrical loads, the downside risk constraint (DRC) method is applied to investigate the EH system function. According to the obtained results, by increasing approximately 2.8% of the operational cost, the risk level can be reduced remarkably. And also, almost 10% of the energy shifted from peak hours to the off-peak time after DRP is applied.     

Optimal scheduling of intelligent parking lot using interval optimization method in the presence of the electrolyser and fuel cell as hydrogen storage system

These days, a new concept called intelligent parking lot (IPL) has been extensively paid consideration to be used in power system industry. Using charge/discharge of electric vehicles (EV), IPLs attempt to exchange power with the upstream grid. In addition to IPL, studied model involves non-renewable and renewable units such as wind turbine, photovoltaic (PV) system, local dispatchable generator (LDG) like micro-turbine and hydrogen storage system (HSS) which are used all together to satisfy energy demand. In this work, optimal scheduling of an IPL has been studied under time-of-use (TOU) rate of demand response program (DRP) in which price of upstream gird is set to be uncertain which uncertainty is modeled via interval optimization technique. This technique transforms uncertainty based model into a deterministic multi-objective model with deviation and average costs as the inconsistency objective functions. Then, applying ε-constraint technique and fuzzy approach, mentioned multi-objective problem is solved. Obtained Pareto results as well as selected trade-off results in various case studies have been compared to prove efficiency of employed techniques. Obtained results revealed that due to positive influence of DRP, increase of average cost of IPL has been reduced up to 2.46% while deviation cost of IPL has been decreased up to 12.49%.     

基于负荷侧响应的含储热热电联产的风电消纳模型

为了有效减少弃风,提高风电消纳能力,该文从负荷侧出发,通过峰谷分时电价策略引导用户的用电方式,达到削峰填谷,优化负荷曲线的目的。同时,在传统热电联产机组中应用大容量储热装置,通过对储热环节的控制,解耦“以热定电”约束,提高系统调节能力。以系统煤耗量最低为目标,构建包含储热的热电联产机组与风电联合出力优化调度模型。该模型考虑系统中的含储热热电联产机组运行成本,同时兼顾储热、负荷侧响应与热电平衡的相关约束等因素,采用基于模拟退火的粒子群算法对模型进行求解,并利用算例比较不同模式下的结果,验证了模型的有效性。     

Energy storage based MG connected system for optimal management of energy: An ANFMDA technique

This paper proposes an efficient hybrid technique for the system modeling and the optimal energy management of the MG with low cost. The novelty of the proposed approach is the combination of the ANFIS and MDA named as ANFMDA that performs the decision making with the multi-objective problem. Moreover, the proposed method is the cost-effective power production of the microgrids and effective utilization of renewable energy sources without wasting the available energy. The strategy is worried about the mathematical optimization problems that include in excess of one objective function to be optimized at the same time. The MDA algorithm optimizes the MG configuration at minimum fuel cost to take care of the required load demand by using the inputs of MG like WT, PV array, MT, and ESS with corresponding cost functions. In the proposed approach, the ANFIS learning phase is employed to predict the load demand. Based on the predicted load demand the minimum annual fuel cost characteristics, the operation cost and also the replacement cost is decreased with all the subsequent points of the MDA. The performance of the proposed method is examined by comparison with the other techniques such as ABC algorithm, DA, and HOMER. The comparison results demonstrate the superiority of the proposed technique and confirm its potential to solve the problem.     

Heat accumulators in CHP networks

In a Combined Heat and Power (CHP) network, it is sometimes optimal to install a device for storing heat from one period of time to another. Several possibilities exist. If the electricity demand is high, while at the same time the district heating load is too small to take care of the heat from the CHP plant, it could be optimal to store heat from peak periods and discharge the storage under off-peak. It might also be optimal to store heat during off-peak and use it under the district heating peak load. The storage is then used for decreasing either the district heating demand or for decreasing the electricity load used for space heating. The paper shows how a mixed integer program is developed for use in the optimization process. As a case study, the CHP system of Malmö, Sweden, is used. Further, a sensitivity analysis is elaborated in order to show how the optimal solution will vary due to changes in certain input data.     

Optimal operational strategy for a future electricity and hydrogen supply system in a residential area

This study introduces a novel framework of an electricity and hydrogen supply system integrating with a photovoltaic power station for a residential area. The non-residential parts including the power grid and non-residential vehicles are added to ensure power balance and bring benefits, respectively. The optimal operational strategy of the proposed framework with considering uncertainties is proposed. The objective function minimizes the expected operational cost (EOC) by reducing the imported electricity from the power grid and increasing exported electricity/hydrogen to non-residential vehicles. Additionally, the demand response program (DRP) is applied in the residential load to achieve operational cost reduction. The uncertainties are modeled via various scenarios by using scenario-based stochastic optimization method. Notably, existing research for similar frameworks both lacks the consideration of uncertainties and DRP, and fails to distinguish the residential and non-residential vehicles with different charging behaviors. The results indicate that 1) The feasibility of the proposed framework is validated which can ensure the power balance of the residential area and reduce the operational cost. 2) The EOC is reduced when considering DRP.     

Optimal day-ahead energy planning of multi-energy microgrids considering energy storage and demand response

Due to the environmental and economic advantages of combined heat and power (CHP) units, their use in power grids has expanded. The entry of CHP into power systems increases the complexity of the economic power flow problem. This complexity is due to the introduction of multiple constraints into problem. A mere electricity supply is not optimal in today's networks, and energies such as heat, power and gas must be planned and managed simultaneously as an energy hub. Therefore, in this paper, an intelligent multi-energy microgrid (MG) consisting of power generation units, CHP units and gas units is modeled for day-ahead energy management (DAEM). The economic distribution problem focuses on the amount of power generation, heat and gas of the units in the system. In contrast, the total generation cost of the system is minimized, and all the equality and inequality constraints of the problem are observed. The proposed microgrid includes various energy-dependent equipment such as CHP units, gas boilers, electricity-to-gas units, power and heat storage units and electric heat pumps. Also, price-based load management was included to reduce costs due to the transfer of information between the consumer and the generator in the context of smartization. Since the above problem is difficult to solve due to various constraints and decision parameters, a newly developed optimization method based on water flows was proposed. The simple movement of water flows on the ground is efficient and optimal and always follows the shortest and fastest path to reach the deepest point. In the proposed algorithm, simple movements of water in routing, a change of direction and even the creation of rapids and vortices were simulated as various mathematical operators. Finally, the proposed model and method were examined in different scenarios. The numerical outcomes demonstrated that, the proposed modeling framework is superior to hub-based multi-carrier microgrid models in terms of power system security. The sensitivity of operational expenses to changes in initial values of energy storage systems (ESS) and thermal storage system (TSS) is proved that the cost of operation reduces as the baseline values of ESS and TSS are reduced to 0.2% of the maximum capacity. Because DAEM performance is less flexible when the primary values are reduced by 0.2% of the maximum value, the system running expenses increase marginally.     

A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: The case of a hospital in Athens

The aim of this paper is to provide an integrated modeling and optimization framework for energy planning in large consumers of the services’ sector based on mathematical programming. The power demand is vaguely known and the underlying uncertainty is modeled using elements from fuzzy set theory. The defined fuzzy programming model is subsequently transformed to an equivalent multi-objective problem, where the minimization of cost and the maximization of demand satisfaction are the objective functions. The Pareto optimal solutions of this problem are obtained using a novel version of the ε-constraint method and represent the possibly optimal solutions of the original problem under uncertainty. In the present case, in order to select the most preferred Pareto optimal solution, the minimax regret criterion is properly used to indicate the preferred configuration of the system (i.e. the size of the installed units) given the load uncertainty. Furthermore, the paper proposes a model reduction technique that can be used in similar cases and further examines its effect in the final results. The above methodology is applied to the energy rehabilitation of a hospital in the Athens area. The technologies under consideration include a combined heat and power unit for providing power and heat, an absorption unit and/or a compression unit for providing cooling load. The obtained results demonstrate that, increasing the degree of demand satisfaction, the total annual cost increases almost linearly. Although data compression allows obtaining realistic results, the size of the proposed units might be slightly changed.     

Long-term optimization of cogeneration systems in a competitive market environment

A tool for long-term optimization of cogeneration systems is developed that is based on mixed integer linear-programming and Lagrangian relaxation. We use a general approach without heuristics to solve the optimization problem of the unit commitment problem and load dispatch. The possibility to buy and sell electric power at a spot market is considered as well as the possibility to provide secondary reserve. The tool has been tested on a demonstration system based on an existing combined heat-and-power (CHP) system with extraction-condensing steam turbines, gas turbines, boilers for heat production and district-heating networks. The key feature of the model for obtaining solutions within reasonable times is a suitable division of the whole optimization period into overlapping sub-periods. Using Lagrangian relaxation, the tool can be applied to large CHP systems. For the demonstration model, almost optimal solutions were found.     

Cost minimization for a local utility through CHP,heat storage and load management

The energy-system optimization model MODEST is described, especially heat storage and electricity load management. Linear programming is used for minimization of capital and operation costs. MODEST may be used to find the optimal investments and when to make them. The period under study can be divided into several linked subperiods which may consist of an arbitrary number of years. MODEST is here applied to a municipal electricity and district-heating system during three five-year periods. Each year is divided into three seasons. Demand peaks, as well as weekly and diurnal variations of, for example, costs are considered. The electricity demand is divided into the three sectors households, industries, and service. The electricity demand may be reduced by energy conservation, replacement of electric heating and load management. The profitability of load management, as well as cogeneration with and without heat storage at different prices of purchased power is calculated. At traditional Swedish electricity prices, the local utility should build a woodchips-fired steam-cycle CHP (combined heat and power) plant. Consumers would find it beneficial to reduce their electricity use by conservation and switching from electric heating to oil and biofuel. If just marginal power production costs are paid, the utility should introduce biomass-fired heat-only boilers instead. Electricity conservation is smaller at these lower prices. Load management is mainly profitable at the first price scheme which includes output-power-related charges. The heat storage should be used threefold: to cover demand peaks, as well as to enable increased CHP output when it is limited by the heat demand or to run heat pumps at cheap night electricity instead of in the daytime. © 1998 John Wiley & Sons, Ltd.     

Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, MG (Micro-Grid) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a CLS (Chaotic Local Search) mechanism and a FSA (Fuzzy Self Adaptive) structure is utilized. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization).     

Risk-constrained scheduling of a CHP-based microgrid including hydrogen energy storage using robust optimization approach

Recently, the integration of various energy resources, including renewable generation and combined heat and power (CHP) units in microgrids, has created the opportunity of off-grid operation with a suitable range of reliability. This paper presents an optimization model to schedule an islanded MG with various resources, including CHP, photovoltaic (PV), and boiler, as the primary energy provision sources besides electric battery storage, thermal storage and hydrogen energy system (HES). The HES has the power-to-hydrogen (P2H) and hydrogen-to-power (H2P) modes, which increases the flexibility of the scheduling. The uncertainty management is the most essential task in the CHP-based MGs scheduling problem, since the power and heat productions are interrelated and can result in economic losses without enough deliberations. Hence, this paper proposes the robust optimization approach (ROA) to cope with the uncertainties associated with the PV production and electric and heat load demands. The robust counterparts are applied to the deterministic problem to create a tractable adjustable robust framework. The problem is structured as a mixed-integer linear programming (MILP) handled by the General Algebraic Modeling System (GAMS) using CPLEX solver. The results verified the effectiveness of the proposed robust counterparts in managing the associated risk. The results illustrated a conscious scheduling strategy under robust conditions. However, the more preserved decisions are taken, the higher operational cost is realized. In this regard, the increment of robustness level from the lowest value (deterministic condition) to the highest value (conservatism condition) increased the operation cost by about 43.29%.     

A modified shuffle frog leaping algorithm for multi-objective optimal power flow

This paper presents an efficient and reliable evolutionary-based approach to solve the Optimal Power Flow (OPF) problem by considering the emission issue. The OPF problem has been widely used in power system operation and planning for determining electricity prices. Therefore, the conventional optimal power flow cannot meet the environmental protection requirements, because it only considers generation cost minimization. The multi-objective optimal power flow considers economical and emission issues. By adding the emission objective in the optimal power flow problem, this problem become more complicated than before and it needs to be solved with an accurate algorithm. This paper proposes an algorithm based on the Shuffle Frog Leaping Algorithm (SLFA) to solve the multi-objective OPF problem. Furthermore, this paper presents a modified SLFA called MSLFA algorithm which profits from a mutation in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The IEEE 30-bus test system is presented to illustrate the application of the proposed problem.     

Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach

Recently, the increasing energy demand has caused dramatic consumption of fossil fuels and unavoidable raising energy prices. Moreover, environmental effect of fossil fuel led to the need of using renewable energy (RE) to meet the rising energy demand. Unpredictability and the high cost of the renewable energy technologies are the main challenges of renewable energy usage. In this context, the integration of renewable energy sources to meet the energy demand of a given area is a promising scenario to overcome the RE challenges. In this study, a novel approach is proposed for optimal design of hybrid renewable energy systems (HRES) including various generators and storage devices. The ε-constraint method has been applied to minimize simultaneously the total cost of the system, unmet load, and fuel emission. A particle swarm optimization (PSO)-simulation based approach has been used to tackle the multi-objective optimization problem. The proposed approach has been tested on a case study of an HRES system that includes wind turbine, photovoltaic (PV) panels, diesel generator, batteries, fuel cell (FC), electrolyzer and hydrogen tank. Finally, a sensitivity analysis study is performed to study the sensibility of different parameters to the developed model.     

An effective heuristic for combined heat-and-power production planning with power ramp constraints

Combined heat-and-power (CHP) production is an increasingly important technology for its efficient utilization of primary-energy resources and for reducing CO₂ emissions. In the CHP plant, the generation of heat-and-power follows a joint characteristic, which makes the determination of both the marginal power production cost (MPPC) and the feasible operating region for the plant more complicated than for the power-only generation plant. Due to the interdependence between heat and power production, the power-ramp constraints, which limit how much the power production of a CHP plant may increase or decrease between two successive periods, may also imply constraints on the heat production. In this paper, we investigate the impact of power-ramp constraints on CHP production planning and develop a robust heuristic for dealing with the power-ramp constraints based on the solution to the problem with relaxed ramp-constraints (RRC). Numerical results based on realistic production models show that the heuristic can generate high-quality solutions efficiently.     

A comprehensive thermodynamic analysis of load‐flexible CHP plants using district heating network

Because of the rapid expansion of intermittent renewable energy, conventional coal‐fired power plants, including combined heat and power (CHP) plants, are required to improve the quick‐response ability to respond the changing demand of the grid. However, the flexibility of CHP plants is not easy to be improved because of the restriction of traditional load variation mechanism. This work presents a comprehensive thermodynamic analysis on the flexibility‐improving scheme using the thermal energy storage (TES) capacity of district heating (DH) network. A typical CHP plant and related DH network were selected as a case study. The flexibility demand under the context of renewables accommodation in the short timescale (counted by minutes) and the operational characteristics of CHP plants were analyzed on the basis of experimental data and thermodynamics. Besides, the influence of heat supply adjustment on heat users' indoor temperature was quantified with a dynamic model, and the thermal inertia of the DH network is discussed. Moreover, a thermodynamic model for the load variation processes simplified with operational characteristics was established to analyze the response ability improvement of CHP plants. Results of the case study show, the scheme can shorten approximately 34% of the response time while almost have no influence on the indoor temperature of heat users.     

Management of fluctuations in wind power and CHP comparing two possible Danish strategies

Both CHP (combined heat and power production) and wind power are important elements of Danish energy policy. Today, approximately 50% of both the Danish electricity and heat demand are produced in CHP and more than 15% of the electricity demand is produced by wind turbines. Both technologies are essential for the implementation of Danish climate change response objectives, and both technologies are intended for further expansion in the coming decade. Meanwhile, the integration of CHP and wind power is subject to fluctuations in electricity production. Wind turbines depend on the wind, and CHP depends on the heat demand. This article discusses and analyses two different national strategies for solving this problem. One strategy, which is the current official government policy known as the export strategy, proposes to take advantage of the Nordic and European markets for selling and buying electricity. In this case, surplus electricity from wind power and CHP simply will be sold to neighbouring countries. Another strategy, the self-supply strategy, runs the CHP units to meet both demand and the fluctuations in the wind scheduling. In this case, investments in heat storages are necessary and heat pumps have to be added to the CHP units. Based on official Danish energy policy and energy plans, this article quantifies the problem for the year 2015 in terms of the amount of surplus electricity, and investments in heat pumps, etc. needed to solve the problem are calculated. Based on these results between the two different strategies, the conclusion is that the self-supply strategy is recommended over the official export strategy.     

Cost-effective operating strategy for residential micro-combined heat and power

It is commonly assumed that dispatch of micro-combined heat and power (micro-CHP) should be heat driven, where the unit turns on when a heat load is present, and turns off or modulates when there is little or no heat demand. However, this heat led operating strategy—typical of large-scale CHP applications—may not be economically justified as scale decreases. This article investigates cost-effective operating strategies for three micro-CHP technologies; Stirling engine, gas engine, and solid oxide fuel cell (SOFC), under reasonable estimates of energy prices. The cost of meeting a typical UK residential energy demand is calculated for hypothetical heat led and electricity led operating strategies, and compared with that of an optimal strategy. Using central estimates of price parameters, and with some thermal energy storage present in the system, it is shown that the least cost operating strategy for the three technologies is to follow heat and electricity load during winter months, rather than using either heat demand or electricity demand as the only dispatch signal. Least cost operating strategy varies between technologies in summer months. In terms of environmental outcomes, the least cost operating strategy does not always result in the lowest carbon dioxide emissions. The results obtained are sensitive to electricity buy-back rate.