Multi-objective optimization of integrated energy systems based on demand response

The interdependency between power and natural gas is so tight, especially where natural gas extraction is economical. Therefore, co-expansion planning is imperative for having efficient systems with minimum cost. In this paper, multistage stochastic co-expansion planning power and natural gas systems is presented. Natural gas load flow (NGLF) is modeled with the Weymouth equation, a non-linear and non-convex problem. In order to overcome the non-convexity of the problem, mixed-integer second-order cone programming (MISOCP) is utilized to solve NGLF. Furthermore, linepack constraints are added to exploit the natural gas stored in the pipeline for co-expansion planning, mainly at the transmission level where voluminous pipelines are used and linepack is noticeable. Natural gas storage is considered in the model to alleviate operational and investment costs. Decreasing the investment and operational costs of co-expansion planning is the objective of the model. Investment decisions can be taken more than once so that investment costs can be divided into the whole planning horizon to avoid an enormous budget at the beginning of the planning horizon. Power and natural gas load growth are taken into account as long-term uncertainties. The proposed model is applied in a real case of southwestern Iran. The results determine that by implementing the proposed model, the investment and operational costs decrease 6.3% and 14%, respectively.

Market-based congestion management in electric power systems with increased share of natural gas dependent power plants

The increasing penetration of natural gas power generation and developing power-to-gas (P2G) technique in recent years have promoted the coupling between the power supply system and the natural gas system than ever before. Given the background, this paper studies the collaborative planning of integrated energy system with gas turbines and P2G stations. First, the energy hub concept is introduced and modeled. A model for an integrated energy system with multiple interconnected energy hubs is next presented. Then, this paper takes the minimum overall cost as planning objectives including investment cost, operation cost and energy shortage cost characterizing the reliability of the integrated energy system, optimizes the capacities of wind generation units, traditional generation units, gas turbines, P2G stations with particle swarm optimization. Case study verified the effectiveness of collaborative planning in economy and reliability of integrated systems.

The interdependence between natural gas and electric power systems is becoming increasingly tight, as the share of natural gas-fired units increases. Within this context, this paper addresses the coordinated expansion planning of natural gas and power systems. We analyze the tradeoff of building natural gas-related facilities (e.g., natural gas pipelines and natural gas-fired generation units) versus electric-power-related facilities (e.g., transmission lines and other generation units). We use a two-stage stochastic optimization model that provides an appropriate balance between accuracy and computational tractability and represents uncertainty pertaining to electricity and natural gas demands. We show the importance of representing uncertainty by computing the value of stochastic solution, which is significant. We analyze the functioning of the model through a small example and a case study based on the IEEE 118-bus system. Computational results confirm the need for detailed representation of both the natural gas and power systems to achieve investment decisions that are well coordinated and optimal.

Building system operation faces the challenge of reducing energy use and implementing a demand response, which can be defined as a temporary modification in energy loads affecting dynamic energy price and reliability information. The heating, ventilation, and air-conditioning (HVAC) system in buildings provides an opportunity for implementing demand response strategies due to the thermal inertia in building zones. However, an HVAC-based demand response is not a prevalent strategy in actual facility management due to the lack of understanding among building operators of their facilities and occupants. Herein, we focus on developing a better understanding of the occupant side by obtaining a reliable prediction of occupants’ thermal satisfaction. We evaluate the prediction performance of a probabilistic model provided in our previous paper using a case study with a subset of the ASHRAE Global Thermal Comfort Database II. The influence of a better prediction of thermal satisfaction on the implementation of the HVAC-based demand response strategy is further discussed. The conventional method overestimates productivity deterioration due to changes in the thermal environment, making it challenging to implement an HVAC-based demand response strategy aggressively. A robust prediction model using a probabilistic approach can solve this problem, allowing building operators to adopt an aggressive stance for implementing a demand response. The results of this study offer fresh insight into the impact of a probabilistic model in the prediction of thermal satisfaction for establishing an HVAC-based demand response strategy.

Due to the increasing penetration of gas-fired units and power to gas facilities, the electrical power system and natural gas system are more and more bi-directionally coupled. To tackle the challenges on the optimal scheduling operation of an integrated gas and power systems (IGPS), this article focuses on developing a novel approach to build a continuous spatial-temporal optimal operation schedule model. In the light of different time constants of the electrical power and natural gas systems, the continuous spatial-temporal optimal operation schedule model of IGPS is formulated in function space. Additionally, Bernstein polynomials are used to reformulate the continuous spatial-temporal optimization problem of IGPS to mixed-integer linear programming. In the study cases, the simulation results of a simple integrated system and a combined IEEE 39-bus power system and Belgian natural gas network demonstrate the accuracy and effectiveness of the proposed model.

Increasing the penetration of renewable generation in distribution systems requires effective management to cater for the variability of demand and renewable generation. In a smart distribution network, Demand Response (DR) plays a crucial role in this but there is a continuing need to demonstrate the benefits and the implementation of DR in home management systems. This paper provides a tool that can be used in the planning stage to investigate the impacts and the benefits of deploying automatic DR under real time pricing on residential consumers. In addition, a real time controller scheme is proposed to manage the operation of each home appliance by deploying load-shift and load-curtailment mechanisms taking into account a degree of flexibility assigned randomly to each consumer.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

The joint operation of natural gas system and power system is an important direction of energy system integration. In this paper, the unit combination modeling method of integrated gas-electric energy system is studied, and the operation constraints such as output, climbing and duration considering unit startup and shutdown state are established. The matrix balance model of power system and natural gas system is established. Based on the generation transfer distribution factor, the tie line security constraints are proposed, and the simplified method of gas electric coupling model of gas turbine based on the piecewise linearization method is proposed. Finally, 6-node power system and 6-node natural gas system are used to build an integrated gas electric energy system. Taking the economic cost optimization as the optimization goal, it is verified that the unit combination constraint in this paper can effectively optimize the unit operation dispatching in the combined system.

As the sharp growth of gas-fired power plants and the new emergence of power-to-gas (PtG) technology intensify the interdependence between electricity and natural gas systems, it is imperative to co-optimize the two systems for improving the overall efficiency. This paper presents a long-term robust co-optimization planning model for interdependent systems, for minimizing total investment and operation costs. Beside generators, transmission lines, gas suppliers, and pipelines, PtGs and gas compressor stations are also considered as investment candidates to effectively handle wind power uncertainties in the power system and compensate pressure losses in the gas network. Furthermore, the proposed model includes a joint N-1 and probabilistic reliability criterion to promote economical and reliable planning solutions. The proposed model is solved via a decomposition approach, by iteratively solving a base-case master problem and two operation subproblems to check N-1 and probabilistic reliability criteria. Numerical case studies illustrate the effectiveness of the proposed robust co-optimization planning approach.

With the widespread installation of smart meters, more and more residents are signing long-term contracts with the grid for demand response (DR) scheme. As an effective way of managing energy consumption about energy prices and system energy balance conditions, the DR scheme contributes to the system cost-saving. In this context, the optimal operation of a multi-energy residential microgrid (MERMG) is studied considering the multi-energy DR scheme. The objective is to minimize the net operating cost under the system’s operational constraints. In the multi-energy DR scheme, the price-based DR (PBDR) is used for power consumption and thermal (heat or cooling) inertia is for the thermal energy consumption. Besides, to guarantee the solution quality as well as relieve the computation burden, the original nonlinear items are linearized to make the operation model a mixed-integer linear programming (MILP) problem. Then it can be then efficiently solved by commercial solvers like Cplex and Gurobi. Finally, by applying the proposed method in the test MERMG and comparing it with the one without the multi-energy DR scheme, the validity of the proposed method is verified.

Electric power systems are subject to failures, due to both deliberate and fortuitous events. This paper addresses the first case in which a disruptive agent aims at maximizing the damage to the network (expressed through the total cost of operation), while the system operator takes the necessary measures to mitigate the effects of this attack. The interaction between these two agents is modeled by means of a bi-level optimization problem. On one hand, the disruptive agent is positioned in the upper-level optimization problem and must decide which elements to render out of service (lines and generators), given a limited destructive budget. On the other hand, the system operator, located in a lower-level optimization problem, reacts to the attack by deploying mitigation measures in order to minimize cost overruns in system operation. Based on the aforementioned dynamic, this paper proposes a novel approach to maximize the resiliency of the power system under intentional attacks through the implementation of distributed energy resources (DERs), namely, distributed generation (DG) and demand response (DR). Three metrics are proposed to assess resilience by assigning DERs in islands generated by the destruction of lines and generators. The results obtained in a didactic 5-bus test system and the IEEE RTS-24 bus test system demonstrate the applicability and effectiveness of the proposed methodology.

This study examines data center characteristics, loads, control systems, and technologies to identify demand response (DR) and automated DR (Open Auto-DR) opportunities and challenges. The study was performed in collaboration with technology experts, industrial partners, and data center facility managers and existing research on commercial and industrial DR was collected and analyzed. The results suggest that data centers, with significant and rapidly growing energy use, have significant DR potential. Because data centers are highly automated, they are excellent candidates for Open Auto-DR. 'Non-mission-critical' data centers are the most likely candidates for early adoption of DR. Data center site infrastructure DR strategies have been well studied for other commercial buildings; however, DR strategies for information technology (IT) infrastructure have not been studied extensively. The largest opportunity for DR or load reduction in data centers is in the use of virtualization to reduce IT equipment energy use, which correspondingly reduces facility cooling loads. DR strategies could also be deployed for data center lighting, and heating, ventilation, and air conditioning. Additional studies and demonstrations are needed to quantify benefits to data centers of participating in DR and to address concerns about DR's possible impact on data center performance or quality of service and equipment life span.

The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics. This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub, with collocated gas-fired units, power-to-gas (PtG) facilities, and natural gas storages, is considered to store or convert one type of energy (i.e., electricity or natural gas) into the other form, which could analogously function as large-scale electrical energy storages. The column-and-constraint generation (C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.

This work focuses on the optimal operation of the integrated gas and electrical power system with bi-directional energy conversion. Considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated gas and power systems. With proper assumptions and simplifications, the problem is transformed into a single stage linear programming. And only a single stage linear programming is needed to obtain the optimal operation strategy for both gas and power systems. Simulation on the test case illustrates the success of the modelling and the beneficial roles of the power-to-gas are analyzed. The proposed model can be used in the decision support for both planning and operation of the coordinated natural gas and electrical power systems.