A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> This study presents an integrated methodology that considers renewable distributed generation (RDG) and demand responses (DR) as options for planning distribution systems in a transition towards low-carbon sustainability. It is assumed that demand responsiveness is enabled by real-time pricing (RTP), and the problem has been formulated as a dynamic two-stage model. It co-optimizes the allocation of renewables [including wind and solar photovoltaic (PV)], non-renewable DG units (gas turbines) and smart metering (SM) simultaneously with network reinforcement for minimizing the total economic and carbon-emission costs over planning horizons. The behavior compliance to RTP is described through a nodal-based DR model, in which the fading effect attended during the load recovery is highlighted. Besides, uncertainties associated with renewable energy generation and price-responsiveness of customers are also taken into account and represented by multiple probabilistic scenarios. The proposed methodology is implemented by employing an efficient hybrid algorithm and applied to a typical distribution test system. The results demonstrate the effectiveness in improving the efficiency of RDG operations and mitigating CO2 footprint of distribution systems, when compared with the conventional planning paradigms. <s> BIB001 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> This paper proposes an artificial neural network (ANN)-based reinforcement learning (RL) maximum power point tracking (MPPT) algorithm for permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs). The proposed MPPT algorithm first learns the optimal relationship between the rotor speed and electrical power of the PMSG through a combination of the ANNs and the Q-learning method. The MPPT algorithm is switched from the online RL to the optimal relation-based online MPPT when the maximum power point is learned. The proposed online learning algorithm enables the WECS to behave like an intelligent agent with memory to learn from its own experience, thus improving the learning efficiency. The online RL process can be reactivated any time when the actual optimal relationship deviates from the learned one due to the aging of the system or a change in the environment. Simulation and experimental results are provided to validate the proposed ANN-based RL MPPT control algorithm for a 5-MW PMSG-based WECS and a small emulated PMSG-based WECS, respectively. <s> BIB002 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> The ongoing deployment of renewable energy sources (RES) calls for an enhanced integration of RES into energy markets, accompanied by a new set of regulations. In Germany, for instance, the feed-in tariff legislation for renewables has been successively replaced by first optional and then obligatory marketing of RES on competitive wholesale markets. This paper introduces an agent-based model that allows studying the impact of changing energy policy instruments on the economic performance of RES operators and marketers. The model structure, its components, and linkages are presented in detail; an additional case study demonstrates the capability of our sociotechnical model. We find that changes in the political framework cannot be mapped directly to RES operators as behaviour of intermediary market actors has to be considered as well. Characteristics and strategies of intermediaries are thus an important factor for successful RES marketing and further deployment. It is shown that the model is able to assess the emergence and stability of market niches. <s> BIB003 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> It is expected that different energy infrastructures and resources to be operated and planned simultaneously in the future smart energy systems. Recently, the concept of energy hub and its corresponding management systems has been proposed in multi-carrier energy systems. The presented study develops a two-stage stochastic mixed-integer linear programming model for a day-ahead energy scheduling of a multi-carrier energy system, considering the time-varying energy price signals and volatility feature of renewable energy resources. In the framework of the proposed method, the objective is set to minimise the energy hub's total cost, while finding the optimal values for decision variables. A set of valid scenarios is considered for the uncertainties of loads and solar energy resources. Finally, the proposed stochastic day-ahead optimisation is tested in a case study situation. In the process of minimising the operational cost, an energy hub operator is able to fix some decisions such as energy exchange with the main grid and unit commitment, before the actual realisation of the uncertain parameters is observed. The unit commitment results also are compared with those produced by a deterministic model for the case in which the electricity flow is bidirectional. <s> BIB004 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> With the growing awareness of environmental implications and fossil fuel crisis, renewable energy harvesting (EH) technology has shown remarkable aptitude in green cellular networking and is expected to be pervasively utilized by telecom operators aiming to reduce carbon footprints. To take the full advantage of renewable EH technology, we proposed an energy sustainable paradigm to address energy self-reliance, eco-sustainability, and minimize the networks energy cost while meeting the quality of service requirements. This paper investigates the techno-economic feasibility of integrated renewable energy powered off-grid cellular base stations (BSs) taking into the account of stochastic behavior of RE generation and traffic intensity for remote areas in Bangladesh. Thereafter, a hybrid energy cooperation framework is formulated to optimally determine the quantities of RE exchanged among BSs via physically installed power cables. Under the proposed framework, each BS is equipped with on-site solar module/wind turbine coupled with an independent storage device, whereas collocated BSs are inter-connected through resistive lines. Extensive simulation is carried out for evaluating optimal system architecture, energy yield analysis, and cost assessment in the context of downlink long-term evolution cellular networks varying different system parameters. Results demonstrate the effectiveness of the proposed system performance pertaining to net present cost and energy savings. Finally, a comprehensive comparison with other schemes is provided for further validation. <s> BIB005 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> Integrated energy system is important for the high-efficient utilization of multitype energy systems. In this paper, the stochastic optimal operation is investigated for the micro integrated electric power, natural gas, and heat delivery system (IPGHS). First, a low-carbon micro-IPGHS is proposed with the comprehensive consideration of renewable generation, carbon-capture-based power-to-gas technology, and the combined power and heat units. Second, a scenario-based optimal operation model for micro-IPGHS is proposed to handle uncertainties in energy demand and renewable generation. In the proposed model, energy transactions between micro-IPGHS and upstream energy systems as well as constraints for battery storage, natural gas storage, and heat storage systems are considered. Finally, a case study is used for the proposed low-carbon micro-IPGHS to validate the optimal stochastic operation approach. The proposed integrated system can effectively utilize the variable clean energy for optimizing the delivery of the green operation in micro-IPGHS. <s> BIB006 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> Green energy is driving the evolutions of energy industry and carbon emission is becoming an important concern. Considering the increasing couplings among various energy sectors, this paper investigates multi-period optimal energy flow and energy pricing in carbon-emission embedded integrated energy systems, including electricity, natural gas, and district heating networks. Firstly, an optimal scheduling model of integrated energy systems was proposed in this paper. The models of DC power flow, natural gas pipeline flow and heating network energy flow are presented and linearized for the optimization problem. Natural gas-fired generators and combined heat and power (CHP) units are modeled as coupling components of electricity-gas and electricity-heating networks. Then, based on the optimal scheduling model, the locational marginal prices (LMP) for electricity, natural gas and heating network are determined. Moreover, the carbon emission caused by energy production has been taken into account in the optimal scheduling and energy pricing process. Finally, case studies on a combined network consisting of IEEE 39-bus system, Belgium 20-node natural gas system and 6-node heating system demonstrate the effectiveness of the proposed model and the impacts of carbon emission on system scheduling and LMP. <s> BIB007 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> The advances in distributed renewable generation technologies in recent years have started to cause load balancing issues in power networks. Distributed energy storage (ES) systems, although seen as a tool to mitigate the stress on local networks, tend to be operated only to minimize the energy cost of their direct owner. In this paper, cooperative game theory is used to construct an energy grand coalition, in which ES system operations are optimized to minimize the coalitional energy cost. Case studies then show that forming energy coalitions is effective in reducing the variability of a local network load profile. The resulting cooperative game is mathematically proven to be balanced, which means the energy cost savings from the cooperative ES operation can be allocated to the players within the energy grand coalition in a manner that disincentivizes them from exiting from the grand coalition to form smaller coalitions. <s> BIB008 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> Focusing on the electricity and thermal energy requirement of contemporary buildings, a joint operation of photovoltaic thermal (PV/T)-based prosumers and a microturbine-based combined heat and power system has been presented to analyse the economics of a grid-connected microgrid (MG) system. The bidirectional flow of the electricity and heat model is considered and is optimally managed using a price-based demand response (DR) scheme. Thermal storage is exploited to ward off the substantial amount of heat wastages that enhance the system's reliability during any disruption of microturbine. The objective functions of both the prosumer and MG operator (MGO) are formulated as a profit maximisation problem where they interact with each other on the basis of DR activity. To establish this strategic decision-making process, the system is modelled as a Stackelberg equilibrium game, where MGO acts as a leader while PV/T prosumers act as a follower. The interaction or contribution of two players in a game is a problem of non-linear optimisation, which is solved by the differential evolution algorithm. In the end, in a case study, it has been proved that the results are quite lucrative for the proposed model. <s> BIB009 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimization Objectives of IES. <s> Increasing interactions between urban electrical and gas systems have the potential to transform smart grids into smart cities, where coordinated dispatch for interdependent urban infrastructure is desirable. This paper proposes an adaptive robust day-ahead energy-reserve co-optimization approach for urban energy systems, in which day-ahead energy-reserve dispatch and real-time energy-balancing regulation are employed as first- and second-stage decisions, respectively. By introducing an auxiliary variable to denote the worst-case balancing cost, we transform the trilevel optimization problem into a single-level model considering generated representative scenarios. Moreover, we propose an iterative algorithm composed of nested problems, where the master problem solves the single-level robust model considering the binding scenario subset identified by the subproblem. In this way, a more computationally tractable model can be obtained, owing to the greatly reduced number of scenarios. Finally, test results on an integrated urban energy network verify the computational superiority of the proposed solution methodology afforded by identifying the binding scenario subset. The economic benefits of the proposed adaptive robust framework are also discussed. <s> BIB010
In park-level IES, there is only one IES that generates, transmits and distributes energy in a certain area. e optimal operation problem of IES is generally expressed as follows: where ( , , ) is the objective function related to energy input , energy storage system and energy allocation coefcient ; the constraint is the balance of energy input and output; min and max are lower and upper limits of energy input; min and max are lower and upper limits of energy storage system output; is the energy allocation coe cient of IES, which generally refers to the proportion of a certain energy output to total energy output. e optimization objectives of a single IES can be summarized as follows: (1) Most of the optimization objectives of IES are to minimize the total operating cost of the system or maximize social welfare BIB008 BIB009 BIB003 BIB004 . BIB002 ere are also some studies whose optimization objectives are optimal carbon emissions or environmental bene ts, to achieve the proportion of green energy in the operation of IES system BIB005 BIB006 BIB007 . ere are still a few studies whose optimization objective is maximal absorption of renewable energy, to improve the proportion of energy supply from renewable energy in IES, so as to achieve better environmental bene ts BIB001 . For regional and cross-regional IES, multiple IES are connected by power transmission network, natural gas BIB010 min ( , , ),