Planning and Operation of Multi-Carrier Energy Networks

Nowadays, researchers focus on multi-carrier energy networks according to their positive characteristics in terms of operation and economic viewpoints. This chapter aims to provide a comprehensive literature review of the cost-saving characteristics of multi-carrier energy networks with different points of view. Firstly, the changes appearing in costs while implementing new sources of energy or using different types of fuels in power plants or DGs are reviewed. Then, the economic aspects of energy exchange in household, commercial, and industrial users (i.e., heat to power or vice versa, gas to power or vice versa) and technologies related to them are discussed. Also, the economic benefits and cost reduction effects of executing energy hubs and their optimal operation, reliability issues, and blackouts or brownouts of grids in such systems are presented. Moreover, this chapter discusses the environmental achievements of implementing multi-carrier networks and their imminent, midterm, and long-term effects on economic benefits. Finally, the role of demand response solutions and their interactions with multi-carrier energy systems are studied.

The operation of multi-carrier energy networks is a realistic viewpoint of the operation of energy systems connected, considering their interconnecting elements. The operation of multi-carrier energy networks has been studied in the literature with different concentrations, including the objectives considered in the operation of such systems, varieties of energy carriers, and uncertainties associated with parameters of such systems. This chapter aims to provide a comprehensive review of the operation of multi-carrier energy systems with different viewpoints including the abovementioned classification. Accordingly, the operation of multi-carrier energy systems with different objectives is discussed, and various energy carriers in multi-carrier energy systems are introduced. The uncertainty-handling methods in operation of multi-carrier energy systems are studied as well as the risk-based option of such systems.

In this chapter, a modern smart energy management system (SEMS) for a multi-carrier microgrid including renewable energy resources, storage system, combined heat and power system, and consumers has been proposed. This microgrid has the capability of exchanging energy with distribution grid and contains both the controllable and uncontrollable loads. For the controllable loads by presenting new operation controlling methods, the consumption of the loads is changing or postponing to another time, with regard to uncertainties of wind and solar generation or energy cost of distribution grid and of course by considering the social welfare of consumer. This operation controlling is done by courage or a rebate in the cost of consumption power. For the optimized operation of the microgrid in the next 24 h, an objective function is maximized. This is done by utilizing the partial swarm optimization method from the point of view of manager of microgrid. At the end, the operation scheduling for optimized beneficiary of microgrid is presented and the results have been analyzed.

Multi-carrier energy networks (MCENs) can participate in different markets and optimize the economical operation of MCEN by optimizing the revenues and costs in these markets. One of the most important markets is the electricity market, where the MCEN purchases or sells electricity to provide the consumers’ demand. Also, there are some ancillary markets (e.g., power balancing services market), capacity market, and local balancing services, which apply extra revenues for MCEN and help the power system operation technically. Moreover, MCEN must consider the use of system charges, tax, and environmental and social obligations in the power management problem. The consumed gas of MCEN consumers is purchased from the gas market. There are imbalance charges for purchased gas on over/under-delivered gas compared to the relevant contracts. In addition to the mentioned markets, there are other markets and incentives such as CO₂ emissions market, energy efficiency market, and low-carbon incentives, which can be considered to improve the MCEN operation technically and environmentally in addition to optimized revenue. More storage capacity and availability of energy prices/incentives for MCEN consumers with communication infrastructure are two reasons of the MCEN higher efficiency in these markets compared to traditional networks, which are described in more detail in this chapter.

This chapter introduces a scheduling algorithm for an active multi-carrier energy system that transacts energy with its downward nonutility energy hubs. The system utilizes combined heat and power units, gas-fired distributed energy systems, thermal and cooling energy storages, electrical storage systems, and energy generation facilities. Further, the plug-in hybrid vehicle parking lots are considered that can transact electricity with the system. The introduced algorithm optimizes the multi-carrier energy system operational scheduling in day-ahead (DA) and real-time (RT) horizons. The optimization process has minimized the operational costs and locational marginal prices of the system and encountered the nonutility energy hub contributions in the operational scheduling of multi-energy carrier system.

An industrial district multiple-energy carrier system is used to assess the introduced method. The optimization procedure successfully reduced the aggregated operational costs of DA and RT horizons and locational marginal prices by about 2.78%.

Nowadays, multi-carrier energy networks are efficient solutions to boost energy efficiency, decrease energy supply cost, and increase the flexibility of the traditional systems. Among the existing elements, energy storage systems and energy conversion facilities play a special role in the optimal operation of multi-carrier energy networks to supply different energy demands. The preferable characteristic of energy storage systems raises the need to use a comprehensive energy management strategy to connect and manage different layers of energy networks in the scheduling process. To this end, this chapter presents an optimal bidding/offering strategy for the economic participation of the hybrid energy storage unit in the multi-carrier energy markets. This strategy is proposed from the perspective of a storage system owner to maximize the profit of the hybrid storage unit. The power-to-gas (P2G) storage, compressed air energy storage (CAES) unit, and power-to-heat (P2H) storage are considered as energy conversion/storage technologies in the form of a hybrid storage unit to participate in multiple energy markets. To validate the effectiveness of the considered method, the presented optimization problem was successfully applied to a realistic case study and was solved using GAMS/CPLEX. According to the results of this study, the hybrid storage unit’s profit is increased by up to 13.2% with the simultaneous use of the CAES unit, P2G storage, and P2H storage compared to the other case studies.

The planning of hydrothermal power systems determines the best possible generation of thermal alongside hydro units to attain the optimum fuel cost of thermal power plants in a short term (1 day) or long term (1 week) in view of several electric system limits and also hydraulic constraints. Owing to complicating variables and decomposable framework of the problem, a dependable approach based upon Benders decomposition algorithm to handle the complex short-term hydrothermal generation planning issue is presented in this chapter. The efficiency of the suggested procedure is validated on a well-known multi-reservoir cascaded hydrothermal system containing four hydro and one thermal units. The approach presented here addresses constraints such as load production balance, unit generation restrictions, reservoir flow balance, reservoir physical appearance constraints, reservoir connection, and water transport delay between the connected reservoirs. Moreover, the cost function of the thermal units is inclusive of the valve point effect. The results are conducted by GAMS solvers and the strengths as well as weaknesses of the proposed method are weighted up with those in this chapter. The obtained simulation results evidently illustrate that the suggested Benders decomposition algorithm can provide great convergence behavior and considerably better solution than other methods associated with the total operation cost and execution time.

Given that fossil fuels are running out, the use of intermittent energy sources, especially wind energy, to generate electricity has grown significantly. Nevertheless, intermittent wind energy causes considerable challenges in the operation of power grid. So, flexible units are needed to cover this drawback of wind energy. Gas-fired power units (GFPU) with fast response ability can meet the need for flexible units in power grids. Meanwhile, flexibility of GFPUs is affected by gas system constraints. Gas storages as a backup option can alleviate the effect of gas network constraints on the operation of GFPUs. This chapter presents a stochastic scheduling model to optimize the operation of electricity and gas networks along with gas storage. In addition, power-to-gas (PtG) technology is included in the model to prevent wind energy dissipation. A test system consisting of electricity and gas networks has been used to investigate the efficiency of the introduced model.

Multi-carrier energy systems (MESs) provide various types of energy to customers like natural gas, electricity, cool, and heat. The interdependency among natural gas, heating, and power systems is rising due to the extensive growth of electrically powered heating facilities and cogeneration systems. Energy hub (EH) performs as a transitional agent amid consumers and suppliers. Therefore, multi-energy incorporation is a prevailing tendency and the EH is supposed to perform a pivotal role in allotting energy sources more effectively. The influence of MESs in distribution systems attracts more and more researchers. The MESs’ uncertainties need to be addressed using efficient methods. This book chapter introduces the interval optimization to deal with the uncertainties. The uncertainties are modeled as interval numbers. Pessimistic predilection ordering and EHs’ pessimism levels are implemented in the optimization in order to make the comparison of interval numbers. The interval optimization minimizes the total cost interval instead of the worst-case scenarios in the robust optimization. It performs computationally better than stochastic optimization, as well. In comparison with the stochastic optimization, a precise probability distribution of random variables is not needed in the interval optimization. Further, it can diminish computational complexity. In this chapter, the stochastic optimization and interval optimization methods are being conducted for evaluation.

In this chapter, a novel methodology is proposed for optimization of an energy hub in Iran (Ganje) to satisfy the electricity, thermal, and cooling loads of a sample residential sector. Different types of distributed generation units and energy storage systems are considered in the mentioned energy hub. The heat water load and heating/cooling loads are considered as thermal demand in the studied system. The produced heat of fuel cell is implemented to provide the thermal energy of energy hub. In this work, the absorption chiller is applied to supply the cooling demand in the energy hub. When the produced heat of fuel cells is more than loads, the extra heat is utilized to store in thermal storages. In addition, when the supplied thermal energy of fuel cells and available energy in thermal storages cannot satisfy thermal loads, waste and natural gas are used to supply thermal energy. Minimizing the studied energy hub’s costs is considered as the main objective of this chapter. The reliability indices are also considered in the mentioned energy hub.

This chapter introduces a framework for expansion planning of multi-carrier energy system that utilizes energy resources to supply the electrical and heating loads. The multi-carrier energy system transacts electricity with the downward active microgrids and considers the impacts of their contribution on its expansion planning exercise.

The proposed risk-averse algorithm minimizes the total investment and operation costs, energy purchased from active microgrids, energy-not-supplied costs, and locational marginal prices of system buses. The hybrid elitist non-dominated sorting genetic algorithm, adaptive genetic algorithm, and non-dominated sorting genetic algorithm are used to optimize the formulated problem and their results are compared.

An industrial district multiple-energy carrier system is utilized to assess the introduced method. Different scenarios of expansion planning are carried out and the optimization algorithm successfully minimized the total investment and operational costs and locational marginal prices of system.

With the expanding and increasing researches about modeling and scheduling of multi-carrier energy systems (MCES) in recent years, the scope of the concept of energy hub as an appropriate solution is increased in order to provide the sufficiency and safety of consumer energy demand. The optimal management of the energy hub requires precise modeling, and the accurate and close-to-reality modeling that is possible just by considering the uncertainties in systems. There are various sources of uncertainty in multi-energy system (MES) which we will discuss in the following. Due to the flexibility of energy hub, appropriate modeling of uncertainty in operation and planning of them enhance these systems’ profitability for various decision makers (customers, producers, operators, etc.). Different modeling methods of these uncertainties have different accuracy, computational burden, and responsibility speed. The goal of this study is to review these uncertain parameters and their modeling techniques in the optimal operation of multi-carrier energy systems (MCES) on the concept of energy hub in order to address the research gap for future works.

Traditional network expansion planning (NEP) relies on treating energy sources, such as electricity, natural gas, and oil, as independent problems. The new solutions to overcome the traditional planning problem is to optimize and integrate energy sources and demands in a joint framework for an efficient generation, storage, and consumption. New techniques and tools for NEP of multi-carrier energy systems are developed. This chapter aims at providing a detailed study of integrating electricity, natural gas, and heat systems, in which the main objective is their network expansion together. The main equations associated with the NEP of multi-carrier energy systems in terms of a mathematical model as a multistage optimization problem are defined. The objective function is to minimize all network expansion costs subject to the technical and nontechnical constraints. Finally, the solution method to solve the multistage NEP of multi-carrier energy systems is explained.