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As a new application form of sustainable energy generation, building-integrated photovoltaics provide great flexibility for energy conservation and emission reduction. However, due to the randomness and volatility from photovoltaic generation, the full exploitation of the advantages of building-integrated photovoltaics has become an important question for the utilities. To address this issue, this paper proposes a novel and comprehensive (combined) water-energy supply system applied to the smart community with building-integrated photovoltaics and two-stage electric-hydraulic dispatching method. It was designed to minimize the electricity consumption cost and power fluctuation while meeting the water demand loads, followed by the iterative algorithm as well as linearized techniques to solve the optimisation model. The Shapley value method was introduced to ensure the fair revenue between the property company and owners. Simulation studies on a building-integrated-photovoltaics community and a 15-node water distribution system indicates that the annual operation cost of the water-electricity combined supply system was reduced by 29.6% while the external grid obtained a better prediction regarding the real-time exchange power. The results confirm the effectiveness of the proposed model on peak shaving and valley filling and fluctuation smoothing.

This paper studies the design and management of a distributed energy system incorporating renewable energy generation and heterogeneous end-users from residential, commercial and industrial sectors. A hierarchical framework is first proposed for the energy demand side management through peer-to-peer exchange of energy information in the real-time market. The heterogeneity of demand flexibility associated with different types of users is reflected in the framework with inter-sectorial interactions modelled by a non-cooperative game. The inherent uncertainty of renewable generation is also considered with the technique of receding horizon optimization. Subsequently, smart contracts and decentralized identifier guaranteed by blockchain technologies are implemented to create a seamless, secured and efficient distributed energy system. Finally, a Singapore-based case study is conducted and the results show that with effective interactions among participants, electricity consumption of the entire energy system closely tracks the generation pattern of renewable resources, resulting in a significantly flattened schedule of grid electricity procurement.

The increasing development of renewable energy sources requires more flexible technologies to be applied in building energy systems and a flexible controlled resource for the power grid. This work focuses on investigating the flexibility potential of building thermal storage and battery energy storage. Firstly, an autoregressive model with exogenous inputs is proposed to forecast the dynamic cooling demand and, based on that, a mixed integer linear model is formulated to optimize the dispatch of building energy systems with minimal operating costs. A factory building located in Huizhou, China is used as a case study. The results show that the ARX model can accurately predict the thermal load hourly. The model’s level of fit is above 81%. The optimization objectives influence the development of the flexibility potential when building thermal storage and battery energy storage are considered. In this work, the economic objective is applied first to discuss the flexibility potential. The results show that, in this studied case, the operational cost decreased by 5.3% by using battery energy storage and further decreased by 4.0% by using building thermal storage. However, the results also reveal that unilaterally pursuing minimal operational costs results in larger peak valley difference of feeder power. The peak-valley difference of the feeder power increased from 714 kW to 1245 kW and 1689 kW respectively when the battery energy storage and building thermal storage were employed for the economic dispatch of the building energy system. Therefore, this work retests the flexibility potential of battery energy storage and building thermal storage by adding a constraint for feeder power difference. The results show that the operational costs can be still reduced greatly without damaging the stability of the power feeder.

Community integrated energy system (CIES) couples multiple energy forms and links, it is important to cope with the inadequacy of flexibility for realizing efficient utilization and reliable supply. Based on the detailed modeling of central energy station, district heating network (DHN) and building loads, a unified scheduling model is established by fully considering the coupling characteristics of the sources, the networks, and the loads. Meanwhile, the flexibilities from adjustable capacity of thermal storage device and the indoor temperature on the demand side, as well as the energy storage of DHN, are utilized to improve the economy. Furthermore, a model predictive control (MPC) based robust scheduling strategy is proposed to maintain and utilize CIES flexibility for enhancing the uncertainty adaptability. The scheduling framework consists of rolling optimization and robust constraint generation. The rolling optimization schedules the system with a better adaptation to flexibility demands, and the robust constraints generate adjustable margin for building demands by modifying the upper/lower and ramping limits. After the linearization of nonlinear terms in the model, case studies are conducted based on the data of a typical day in summer. The results show that the unified source-network-load scheduling strategy can give full play to the flexibilities of different energy links with a better operation economy. Additionally, the MPC-based optimization can well adapt to the rolling forecasting uncertainties, and the schedule generated by advance reserving means has stronger robustness for real-time errors.

Thermal loads are an important source of flexibility at a residential customer level. The uncertain economic value of residential demand response (DR), and the rising customer data privacy concerns, require non-intrusive and economical approaches to harness flexibility. Baselines are essential for evaluating DR activations, however, the frequent use of flexibility makes them less accurate. In this paper, we first propose a baseline estimation method based solely on aggregated behind the meter data, which does not require additional knowledge of the portfolio’s parameters. It is suited for frequent DR activations, and relies on a combination of linear interpolation, forward-backward autoregression and load decomposition. The method is then used to evaluate DR activations, in order to construct, and continuously update, a model for the response and the rebound behavior of the loads. A portfolio of 138 real residential customers equipped with electric heaters, and a large number of DR experiments, were used to verify the proposed approach. The response model, fitted with the experimental results, shows a strong dependency of the load reduction potential on time of day and ambient temperature, with a maximum load reduction equal to 1.2 kW per household. Validation results confirm that the fitted model can be used to estimate the response with a good accuracy. Finally, a model to describe and shape the rebound behavior of the loads is proposed and validated with real experiments.

An unmanned aerial vehicle (UAV), commonly known as a drone, offers the advantage of speed, flexibility, and ease in delivering goods to customers. They are particularly useful for tasks that are dull, hazardous, or dirty. Whether the use of drone delivery is beneficial to the environment and cost saving is still a topic under debate. Ideally, drones yield lower energy consumption and reduce greenhouse gas emissions, thus reducing the carbon footprint and enhancing environmental sustainability. In this research, we analytically study the impact of UAVs on CO2 emission and cost. We propose a mixed-integer (0–1 linear) green routing model for UAV to exploit the sustainability aspects of the use of UAVs for last-mile parcel deliveries. A genetic algorithm is developed to efficiently solve the complex model, and an extensive experiment is conducted to illustrate and validate the analytical model and the solution algorithm. We find that optimally routing and delivering packages with UAVs would save energy and reduce carbon emissions. The computational results strongly support the notion that using UAVs for last-mile logistics is not only cost effective, but also environmentally friendly.

Threatened by natural disasters and man-made attacks, the resilient operation of power systems has drawn increased attention, which gives rise to a greater demand for power generation assets with high operational flexibility, such as natural gas power plants (NGPPs). This, in turn, results in a greater proportion of NGPPs and greater interdependence between power and gas systems. As a consequence, the modeling of the interactions between power systems and natural gas systems to achieve operational resilience in power systems becomes extremely vital. This topic has been discussed by quite a few researchers; however, previous studies suffered from two major drawbacks, namely (1) they assumed the existence of only one utility that has full control authority over the power system and gas system; (2) the economic interactions between power systems and gas systems have been neglected, which goes against current industrial practice. In this study, the power system and gas system are regarded as two individual utilities and their physical and economic interactions are modeled by considering the fuel consumption of the NGPPs and gas contracts and by guaranteeing the fuel availability of NGPPs in the pre- and post-contingency stages, respectively. The proposed model is developed based on a two-stage robust decision-making framework to optimize the operational performances of power systems under the worst-case N-k contingencies. To deal with the binary variables introduced by the linearization of the Weymouth equation and the on/off grid operation of generators, the nested column-and-constraint generation (NC&CG) algorithm is adopted. The necessity of considering economic and physical interactions between power systems and natural gas systems and the effectiveness of the proposed model and algorithm are verified by numerical simulations of two test systems.

With the integration of high shares of distributed generators (DGs), it is increasingly difficult to cope with the voltage violations and puts forward a higher requirement for the operational flexibility in active distribution networks (ADNs). Soft open point (SOP) can realize accurate power flow control and continuous voltage regulation, which is usually operated by the centralized control strategy. However, the heavy burden of communication and complex global optimization process hinder its application on fast voltage control. This paper proposes a combined decentralized and local voltage control strategy of SOPs to rapidly cope with the frequent voltage fluctuations. Based on the dynamic network partition results, the decentralized optimization is applied to regulate active power transmission of SOPs among the connected areas. The control curve is optimally tuned to locally regulate the reactive power outputs of SOPs by using the intra-area information. Compared to the widely-used centralized strategy, the proposed combined control strategy of SOPs can effectively reduce the communication and computational burden with acceptable voltage control performance. Finally, the effectiveness of the proposed control strategy of SOPs is validated on the modified PG&E 69-node distribution system and modified IEEE 123-node distribution system.

Demand response is recognised as a potentially cost-effective means for providing the increasing amounts of flexibility needed in power systems with increasing penetrations of renewables. However, existing techno-economic approaches for flexible power systems modelling do not recognise that demand response, where it affects the comfort of the end-user, is heavily influenced by the biases and preferences of consumers. That is, demand response is modelled under the assumption that end-users are always rational and active economic agents. This has consistently resulted in seemingly inexplicable gaps between modelled and observed results for demand response schemes. Behavioural economics, which applies psychological insights into economic modelling, has been proposed to address this problem. This work determines the suitability of behavioural economics as an approach for modelling demand response before reviewing the application of behavioural economics ideas in energy related studies. Then, the effect of different customer biases and preferences on the modelling of demand response is studied, through adaptation of an existing techno-economic demand response model. The results demonstrate that consideration of biases can impact modelling of demand response provision, especially when demand for an energy end-service is high. These findings are of interest to procurers of demand response. Results also demonstrate that appealing to customers pro-social preferences can be an effective way of soliciting demand response. This can have strong regulatory implications as reliance on incentives to procure demand response, as the techno-economic approach assumes, may ‘crowd-out’ such pro-social attitudes, increasing the overall system cost of eliciting demand response and, hence, of operating the electricity system.

Utilizing multiple energy supply solutions and the thermal inertia of buildings are considered as an efficient method for improving energy conservation and the operational flexibility of Combined Heat and Power (CHP) for wind power integration in a district integrated heat and power system (DIHPS). However, to truly take advantage of these, many factors such as the energy price, the variable efficiency of devices, and especially the different heating regulation modes (the quantity regulation and the quality regulation mode) are imperative to be considered for variable situations of the practical project. Therefore, in this paper, an integrated hydraulic-thermal model of the district heating network (DHN) under the quantity regulation mode is introduced, and a novel thermal energy flow model with transmission time delay under the quality regulation mode is firstly proposed to simulate the dynamic thermal energy distribution of the DHN with multiple heat sources. Then, in terms of different heating regulation modes, the different thermal capacity of buildings to increase the flexibility of the DIHPS is studied. Moreover, an optimal scheduling method considering different energy stations and the thermal inertia of buildings under different heating regulation modes is proposed to guide the operation of the DIHPS in variable actual engineering applications for the sake of operational economics and wind power utilization. Finally, numerical cases have been compared based on a modified testing system. The results demonstrate that under the quantity regulation mode the coordination of energy stations effectively reduces the daily operation cost by 17.4%. The thermal inertia of buildings give a saving rate of 6.4% and he penalty cost of wind power is reduced by 36.3% further. Moreover, the total thermal capacity of buildings to reduce the operation cost and the wind power curtailment is less under the quality regulation mode. It results that the daily operation cost increases by 4.1%. In addition, towards the DIHPS with multiple energy stations in our case, the thermal inertia of pipes cloud not reduce the daily operation cost.

The enrichment in building operation data has enabled the development of advanced data-driven methods for building energy predictions. Existing studies mainly focused on the utilization of supervised learning techniques for model development, while overlooking the significance of feature engineering. Feature engineering are helpful for reducing data dimensionality, decreasing prediction model complexity, and tackling the problem of corrupted and noisy information. Considering that each building has unique operating characteristics, it is neither practical nor efficient to manually identify features for model developments. Data-driven feature engineering methods are thus needed to ensure the flexibility and generalization of building energy prediction models. Using operation data of real buildings, this paper investigates the performance of different deep learning techniques in automatically deriving high-quality features for building energy predictions. Three types of deep learning-based features are developed using fully-connected autoencoders, convolutional autoencoders and generative adversarial networks respectively. Their potentials in building energy predictions have been exploited and compared with conventional feature engineering methods. The study validates the usefulness of deep learning in enhancing building energy prediction performance. The research results help to automate and improve the predictive modeling process while bridging the knowledge gaps between deep learning and building professionals.

Demand response can be an effective means for power system operators to compensate fluctuating renewable generation, to avoid grid congestion, and to cope with other contingencies. Buildings equipped with electric heating systems can provide demand-response services because their electricity consumption is inherently flexible due to their thermal inertia. This paper reports on the results of a large-scale demand-response demonstration involving a population of more than 300 residential buildings with heat pumps. Based on a procedure to autonomously estimate the electric flexibility of individual systems from energy meter data and outdoor air temperature measurements, we show how the aggregate demand-response potential of the systems can be quantified and predicted. The results of various experiments illustrate that load reductions of 40–65% of the total load can be achieved by throttling the heat pumps, and that these load reductions can be delivered precisely with a median absolute percentage error of below 7%. In addition, a rebound damping strategy is proposed that was shown to reduce the peak rebound power by 50% in practice.

This paper proposes a two-stage stochastic optimization model to support an aggregator of prosumers in the definition of bids for the day-ahead energy and secondary reserve markets. The aggregator optimizes the prosumers’ flexibility with the objective of minimizing the net cost of buying and selling energy and secondary reserve in both day-ahead and real-time market stages. The uncertainties of the renewable generation, consumption, outdoor temperature, prosumers’ preferences, and house occupancy are modeled through a set of scenarios. For a case study of 1000 prosumers, the results show that the proposed bidding strategy reduces the costs of both aggregator and prosumers by 40% compared to a bidding strategy typically used by retailers.

Green smart factories will be one of the key elements of the future smart grids. They will play an active role in managing the volatility of power generated by renewable energy sources (RES). In order to do that, the green smart factories must set up energy management systems to control the production processes according to both the on-site power generation by RES and external signals sent by the system operators. The aim of the study is to size energy storage systems and production buffer stocks as the flexibility options, allowing the highest integration of power generated by volatile energy within a manufacturing system. The concept of the net zero energy factory is considered. Algorithms for coordinating the flexibility options optimally have been developed. These algorithms are based on mixed-integer linear programming and aim to maximize the matching between RES power generation and flexible demand. A case study is used to verify the framework results. Key performance indices, such as the system self-sufficiency index and the system self-consumption index have been considered for evaluating the solutions developed. Finally, sensitivity analysis has been performed to assess the robustness of the solutions found.

Polygeneration energy systems are proven to be a reliable, competitive and efficient solution for energy production. The recovery of otherwise wasted energy is the primary reason for the high efficiency of polygeneration systems. In this paper, the integration of a high-temperature heat pump within a trigeneration system is investigated. The heat pump uses the low-temperature heat from the condenser of the absorption chiller as heat source to produce hot water. A numerical model of the heat pump cycle is developed to evaluate the technical viability of current heat pump technology for this application and assess the performance of different working fluids. An exergy analysis is performed to show the advantages of the novel trigeneration system with respect to traditional systems for energy production. Moreover, a levelized cost of electricity method is applied to the proposed energy system to show its generic economic feasibility. Finally, actual energy demand data from an Italian pharmaceutical factory are considered to evaluate the economic savings obtainable with the integrated system, implemented in a case study. A two-level algorithm is proposed for the economic optimization of the investment. The synthesis/design problem is addressed by a genetic algorithm and the optimal operation problem is solved by a linear programming method. Results show that the integration of a high-temperature heat pump within a trigeneration system provides flexibility to cover variable energy demands and achieve valuable economic and energy performances, with global cost savings of around 40% with respect to separate production and around 10% with respect to traditional cogeneration and trigeneration systems.

The development of smart grids represents a major breakthrough in the management of electric power and drinking water systems. On the one hand, smart grids have contributed to the development of energy efficiency and demand side management mechanisms such as Demand Response, making it possible to reduce peak load and adapt elastic demand to fluctuation generation. On the other hand, smart water networks and sophisticated Supervisory Control and Data Acquisition systems in the water industry have allowed one to optimize, control and monitor the water flow throughout its entire process. Being a highly energy intensive industry and having an electrical flexibility by the presence of storage elements such as tanks, drinking water systems have the ability to address energy efficiency mechanisms such as Demand Response. In this paper, the French demand response mechanism in spot power markets is presented. Then, a chance constrained problem is formulated to integrate water systems flexibility to power system operation, under water demand uncertainties. Numerical results are discussed based on a real water system in France, demonstrating the relevance of the approach in terms of financial benefits and risk management.

An interactive multi-period planning model is presented for sustainable urban water and energy supply, taking into account surplus output from grid-connected residential photovoltaics as a part of the water-related energy mix. The two-level mixed integer linear model finds the optimal strategic and operational decisions for a desalination-based water supply system driven by hybrid energy sources and determines the evolution of the potential capacity of a renewable energy technology over the planning horizon. It considers demands, supply systems configuration, resources capacities and electricity tariffs as well as economic, subjectivity and technical criteria for uptaking rooftop photovoltaic systems. The model was then applied to Perth (Australia) and solved for alternative scenarios. The results show operational flexibility and decentralised planning of the integrated system lead to $251,515,132 less discounted total cost over centralised water supply system operated in fixed mode. They also indicate that decentralised scenario results in 42,765.1 kW higher potential photovoltaics uptake capacity on average in each year over the planning horizon in the case study area compared to centralised scenarios. However, based on the results of the sensitivity analysis, the selection of this scenario as the best alternative highly depends on the parameters values associated with subjectivity criterion and operational and maintenance cost of flexible mode of operation.

Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth.

Given the current influx of intermittent renewable energy sources due to the German Energiewende, additional flexibility is needed to guarantee a reliable power supply by varying electricity generation. Battery energy storage systems are playing an important role for the grid stabilization in Germany. This paper analyses the hybrid concept of combining batteries with power-to-heat technology in the environment of primary control reserve. A comparative economic assessment of hybrid and stand-alone battery systems is discussed by making use of a simulation model in MATLAB®. The economic feasibility of a sustainable business model was investigated in accordance with the German regulatory framework. By comparing to a stand-alone battery system providing primary control reserve, simulation results revealed that the combination with a power-to-heat system is another option for providing a reliable frequency regulation. Despite the additional cost of power-to-heat units, grid fees, taxes and expected shorter lifetime of the battery cells, the hybrid solution leads to an improved profitability scenario for investors when compared with a stand-alone battery in a PCR market.

With the growing use of renewable energy sources, the stability of electrical power systems can be seriously affected by fluctuations in the available power. As one of the potential solutions for this new challenge, the energy flexibility of buildings has become a focus for research and technological development. Most studies have focused on single buildings, with only a few studies on building clusters in which the building models were usually oversimplified in that they did not consider different building types or their thermal characteristics, their occupancy or their occupants’ behaviour. In this paper, we describe a data driven approach to simulating a generic building cluster that could resemble any mix of building archetypes and occupancy. The energy flexibility potential of apartment building clusters was estimated by using data from surveys and available statistics in Denmark for the worst case scenario, i.e. when the end users do not allow any disturbance when they are at home, so that energy flexibility is only available when residents are not at home. In this scenario, no energy flexibility is assumed when buildings are occupied, which yields a conservative estimation. The uncertainty of the energy flexibility potential due to uncertain occupancy and various archetypes was quantified for different scales of building cluster. The resulting hybrid-model is a combination of a building model and an occupancy model and includes the different factors that influence the potential energy flexibility of buildings. The results show that the uncertainty of the energy flexibility decreases when the aggregated number of buildings increases. The uncertainty of energy flexibility was less than 10%, when about 700 households were aggregated. This approach can be used to simulate building energy flexibility for district or even regional level energy planning when the intention is to use the available flexibility to address the challenges caused by fluctuation in the power available from renewable energy sources.

The European electricity markets’ integration aims at the market coupling among interconnected power systems and the enhancement of market competitive forces. This process is facilitated by the adoption of a common clearing algorithm among European power exchanges, entitled EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), which however lacks to capture critical technical aspects of power systems, as done by the unit commitment problem including start-up and shut-down decisions, time constraints (minimum on- and off-times), as well as the consideration of ancillary services. This paper presents an optimization-based framework for the optimal joint energy and reserves market clearing algorithm, further utilizing the hourly offers module of the EUPHEMIA algorithm. In particular, through the formulation of a mixed integer linear programming (MILP) model and employing an iterative approach, it determines the optimal energy and reserves mix, the resulting market clearing prices, and it calculates the welfares of the market participants. The model incorporates intra-hourly power reserve constraints, as well as introduces new market products such as the option of forming linked groups of power units, aiming at supplying additional flexibility in the decision-making of the market participants. The model applicability has been assessed in the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed optimization framework can provide useful insights on the determination of the optimal generation and interconnection portfolios that address the new market-based operational challenges of contemporary power systems subject to technical and economic constraints.

With the development of smart grid and electricity market, the uncertainty for power flow is greatly aggravated, and thus leads to a great challenge on the traditional expansion methods for distribution systems to satisfy the future demands. In this paper, a data-driven multi-state distribution system expansion planning (DSEP) model is explored. Innovatively, amplitude values and profiles of uncertain factors in distribution systems are considered separately. Based on the massive historical measurement data, kernel density estimation and adaptive clustering are utilized to aggregate the typical amplitudes and profiles of time-varying variables respectively without prior knowledge. Consolidating all the uncertain factors, a multi-state model is established which extends DSEP into the deterministic initial planning and the probabilistic re-planning. The minimization of the overall planning cost is considered as the objective, which takes the initial planning costs and the expected costs of the initial plans being adapted to other future states into account. In this way, the flexibility of DSEP can be greatly enhanced and extra investments caused by frequent adjustments of plans are reduced. To avoid the rapid growth of CPU time due to multi-state model utilization, an integrated differential evolution and cross entropy algorithm implemented on a three-hierarchy parallel platform is proposed. The feasibilities of the proposed data-driven multi-state DSEP model and the parallel integrated solution method are demonstrated by numerical studies on a realistic 61-bus distribution system.

The decreasing cost of energy storage and increasing demand for local flexibility are opening up new possibilities for energy storage deployment at the local level. Community energy storage (CES) is expected to contribute positively towards energy transition while accommodating the needs and expectations of citizens and local communities. Yet, the technological and societal challenges of integrating CES in the largely centralized present energy system demand for socio-technical innovation. In this article, we develop and discuss several configurations of CES. Applying system innovation and socio-technical transition frameworks and conceptualizing CES as a complex socio-technical system, different dynamics of CES in the energy systems such as coordination and interaction among actors and components of CES and the larger energy system is explored. The responsible research and innovation (RRI) framework can provide a new discourse in design and implementation of CES, facilitating the transition to a sustainable, reliable, inclusive and affordable future energy system.

Efficient oxygen excess ratio (OER) control is of great importance for proton exchange membrane fuel cell because it is closely associated with the economic efficiency and safety. As widely investigated, OER control is challenging due to the difficulties of system nonlinearity, parametric uncertainty and load disturbances. In this paper, an underlying difficulty for OER control is addressed by pointing out the overshoot response. To this end, this paper employs active disturbance rejection control which is able to handle the various difficulties in a data-driven manner. It treats the nonlinearity, uncertainty and disturbances as a lumped term, which is then estimated online via analyzing the real-time data. The estimated lumped term is canceled timely such that the remaining dynamics behaves like an integrator without overshoot term therein. The data-driven and conventional proportional-integral controllers are tuned and compared based on the linearized transfer function model, showing the potential superiority of the proposed method in terms of the uncertainty and disturbance rejection, anti-windup and overshoot reduction. The nonlinear simulation based on the nonlinear mechanism model further demonstrates it good flexibility under different operating conditions. Moreover, it requires less compressor movement efforts, leading to a dynamic energy-saving effect and thus prolonging the durability and lifetime of the compressor.

Planning of future energy systems with higher prevalence of wind and solar energy requires a careful representation of the temporal and operational characteristics of the system in the investment planning model. This study aims to identify the aspects that should be considered when selecting the representation for a particular system. To demonstrate the impacts that various model representations have in terms of model accuracy and computational effort, we carry out case studies on two test systems implemented within the Backbone energy systems modelling framework. The results show that the temporal and operational representations have different benefits and weaknesses in different system types. The findings provide general guidelines on the relative importance of different model details, depending on the characteristics of the system under study. For example, some temporal sampling strategies can better capture long-term storage needs, while others are more suitable for short-term storage modelling. Likewise, solar-dominated and wind-dominated systems differ in their methodological requirements. Furthermore, the interactions between energy sectors and the operational limits of the technologies for sector coupling should be correctly captured, as they significantly impact on the value of different technologies and their flexibility. Finally, we recommend testing several temporal and technical representations for each particular system in order to ensure the feasibility of the selected method for that purpose. The findings and recommendations inform energy system modellers about improvements that will facilitate higher quality planning results.

There is an intrinsic value in higher integration of multi-carrier energy systems (especially gas and electricity), to increase operational flexibility in the electricity system and to improve allocation of resources in gas and electricity networks. The integration of different energy carrier markets is challenging due to the existence of physical and economic dependencies between the different energy carriers. We propose in this paper an integrated day-ahead multi-carrier gas, electricity and heat market clearing which includes new types of orders and constraints on these orders to represent techno-economic constraints of conversion and storage technologies. We prove that the proposed market clearing gives rise to competitive equilibria. In addition, we propose two decentralised clearing algorithms which differ in how the decomposition of the underlying centralised clearing optimisation problem is performed. This has implications in terms of the involved agents and their mutual information exchange. It is proven that they yield solutions equivalent to the centralised market clearing under a mild assumption of sufficient number of iterations. We argue that such an integrated multi-carrier energy market mitigates (spot) market risks faced by market participants and enables better spot pricing of the different energy carriers. The results show that conversion/storage technology owners would suffer from losses and/or opportunity costs, if they were obliged to only use elementary orders. For the test cases considered in this article, sum of losses and opportunity costs could reach up to 13,000 EUR/day and 9,000 EUR/day respectively, compared with the case where conversion and storage orders are used.

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ European Directives are incentivizing consumers to play an active role in the electricity system and to collaborate to maintain its stability, which has been historically provided by large generation power plants. However, it is not easy for the System Operator to handle the coexistence of consumers and generators in the same markets. Under these circumstances, a new actor allows small residential and commercial consumers to participate in flexibility markets: The Demand Aggregator. However, balancing markets opened to Demand Aggregators still present several barriers that do not allow their practical participation. This study analyzes barriers and enablers of four European electricity markets and proposes a new market framework that would enhance Demand Aggregators’ participation. To validate the proposed market and to understand the economic potentials of aggregated small tertiary buildings, a Demand Aggregator is simulated using real building’s consumption data. Results show that technical requirements to participate in balancing markets such as the minimum bid size, the symmetricity of the offer and the product resolution strongly affect incomes for Demand Aggregators. However, neither in the proposed market, the creation of a Demand Aggregator whose business model is focused on small tertiary buildings does not seem realistic due to low incomes in comparison to the fixed costs necessary to enable Demand Response, especially if only the air conditioning system is considered.

A smart integration of electric vehicles (EVs) in the future energy system will be crucial in decarbonizing the energy sector. Bidirectional EVs can provide flexibility for the system and generate revenues for the user through multiple use cases. We model both exclusive photovoltaic (PV) self-consumption optimization and the combined usage of PV self-consumption optimization and arbitrage trading for a household with an unmanaged, smart, and bidirectional charging EV in a linear (LP) and mixed-integer linear programming (MILP). Since power flows in a typical household are low, varying non-linear charging and discharging efficiencies of the bidirectional EV in the MILP result in more realistic revenues that are 30% lower than in the LP with fixed efficiencies. For a typical German household using a bidirectional EV for optimizing PV self-consumption, these revenues are about 310 €/a, mostly generated during the summer. Arbitrage trading well complements this vehicle-to-home use case in the winter months, resulting in revenues up to 530 €/a. These significant revenue potentials can lead to more profitable and interactive EVs incentivizing users to change from internal combustion vehicles to electric mobility.

The modern power grid faces multiple challenges due to an increase in the adoption of renewable generation, such as dynamically balancing supply and demand at different time scales. Demand side management in buildings plays a vital role in achieving this balance because buildings can provide grid services through a variety of building assets. However, the development of grid-interactive, efficient buildings is still in its infancy, and a systematic and holistic understanding of grid service delivery strategies in terms of energy efficiency, load shifting, load shedding and load modulating is still limited. This paper is a comprehensive review of the development and application of building-level control strategies for utilizing heating, ventilating, and air conditioning systems to provide grid services. These strategies have been investigated through numerical and experimental studies. Control algorithms, such as heuristic rule-based control and model-based control, have been used to enable the automatic control delivery of grid services. The advantages and disadvantages of the strategies are summarized and discussed. Research trends are also identified, which include considering predicted mean vote-based and occupant-based thermal comfort, modeling of occupant behavior, integrating power grid operations with building control, and combining different demand flexibility modes in the control design.

Green hydrogen is viewed as a promising energy carrier for sustainable development goals. However, it has suffered from high costs hindering its implementation. For a stakeholder who considers both renewable energy and electrolysis units, it is important to exploit the flexibility of such portfolios to maximize system operational revenues. To this end, an electrolyser model that can characterize its dynamic behavior is required in both electric and thermal aspects. In this paper, we develop a comprehensive alkaline electrolyser model that is capable of describing its hydrogen production properties, temperature variations and state transitions (among production, stand-by and off states). This model is further used to study the optimal dispatch of an electrolyser based on a real-world hybrid wind/electrolyser system. The results show the model can effectively capture the coupling between thermal–electric dynamics and on–off performance of an electrolyser. The flexible operation strategy based on this model is proven to significantly increase daily revenues under different spot price conditions for electricity. Comparing the model with the ones derived from conventional modeling methods reveals this model offers more operating details and highlights several operational features, such as the preference for working at partial load conditions, although at the expense of more computing resources. It is suggested to use this model in studies related to energy integration, operation planning and control scheme development, in which the multi-domain dynamic properties of electrolysers in electricity/gas/heat need to be properly characterized. A sensitivity analysis on key parameters of such electrolyser system is also introduced to connect the daily operation with long-term planning.

Electromobility in public bus systems is growing rapidly and experiencing a fundamental transformation in their infrastructure and operations. The dilemma of limited driving range and charging time of battery electric buses (BEBs) hinders their adoption. A novel approach to address BEB limitations is to utilize dynamic wireless charging (DWC) technology that allows buses to charge while in motion. This paper aims to analyze robust strategic planning of DWC and BEB fleet scheduling based on a real bus network at Binghamton University. The problem is first formulated as a new deterministic mixed-integer linear programming model to simultaneously optimize both the charging planning problem and fleet scheduling problem in an integrated fashion. To address the uncertainty of energy demand and charging time, a robust counterpart model (RCM) has been derived. To increase RCM flexibility, the battery status variable is formulated in a cumulative form. Dependent and independent budget uncertainty sets have been developed to control the robustness. A sensitivity analysis has been conducted to study the system behavior in response to different charging types, auxiliary energy demand, depth of discharge, charging options at terminals, battery degradation, and electricity cost. The deterministic model shows that eight homogeneous BEBs are required to operate on the selected routes with a battery capacity of 54.01 kWh and a total cost of $3,636,347. The results show that joint planning of charging infrastructure and fleet scheduling can save 19.2% of total cost compared to disjoint planning. The RCM results in 10 BEBs to ensure feasiblility against uncertainty.

The proper operation strategy is of great importance for photovoltaic-battery (PVB) systems to achieve desirable performance. Therefore, it’s necessary to identify and evaluate the characteristics of different operation strategies for engineering application. In this study, three operation strategies including the dynamic programming (DP)-based strategy and two widely used rule-based strategies, namely the maximizing self-consumption (MSC) strategy and time-of-use (TOU) strategy, are compared comprehensively for the grid-connected PVB system of an office building. Several important performance aspects, including battery charge/discharge process, techno-economic performance, energy distribution, battery aging and impacts on utility grid, are analyzed and compared. The results show that the DP strategy has greater flexibility than the rule-based strategies to adapt to different PV-load distributions and electricity pricing mechanisms, and has the advantage of achieving better economic performance and reducing burden on the grid. Although the economic performance of the MSC strategy is relatively poor, it has the highest self-consumption rate (SCR) and self-sufficiency rate (SSR), thereby having the advantage of using PV generation in timely manner to suit load demand. In addition, the MSC strategy results in the least battery aging, while having the greatest negative impact on the utility grid. The TOU strategy only outperforms the MSC strategy in economic performance under the condition of relatively low battery cost (

A combined heat and power plant (CHP) has the ability to provide ancillary services and therefore, can contribute to the improvement of flexibility and reliability of the power system. To motivate CHPs to provide flexibility services, this work investigated the potential benefit for CHPs from participating simultaneously in day-ahead and frequency regulation markets. A new CHP model was proposed, which considers both heat recovery from flue gas condensation and thermal energy storage. Based on the multi-market optimization using real market prices, it can be concluded that providing the frequency regulation service in addition to day-ahead trading can increase the annual profit of CHPs, which was 2.75% for the studied CHP. Meanwhile, the benefit was clearly affected by the heat demand as both high and low heat demand seasons (e.g. in winter and summer) can limit the flexibility provided by the CHP. In addition, the sensitivity analysis was carried out to assess the impacts of key factors, including electricity price, heat demand, cost of electricity, and bid size for frequency reserve services, on the benefit from the participation in both markets. The price difference between the day-ahead and frequency regulation markets and the cost of electricity generation were found to have clear impacts on the benefit of the CHP.

In recent years, demand side measures have been increasingly considered to provide flexibility services in different timescales (seconds, minutes, or longer timescale) and thereby improve the reliability and overall energy efficiency of power systems. However, the existing studies about multiple grid flexibility services only focus on the generation or storage resources, without considering the variety, controllability, and flexibility of different loads. Few studies have investigated the economic benefits and contributions of building energy flexibilities with fast and slow response speeds to different flexibility services. Therefore, this study develops a novel model-based predictive dispatch strategy for hybrid building energy systems to maximize the economic benefits in electricity markets of multiple services. The energy flexibilities of buildings are transformed to the bids for energy trading, peak charge and ancillary services in the electricity market. The system characteristics and the comfort (or preferences) of occupants regarding multiple flexibility resources, including dimmable lighting systems, HVAC systems, electrical vehicles and stationary batteries integrated with PV are considered. Tests are conducted to evaluate the performance of the strategy and the impacts on building operation, using real-time TRNSYS-MATLAB co-simulation. Test results show that electricity costs can be reduced by up to 26.1% when fully utilizing multiple revenue streams in an electricity market. The impacts of uncertain and high-granularity grid control signals on the indoor environment, the charging requirements of EVs and state of charge (SOC) of battery are negligible while the expected building power modulation following the real-time power grid control signals can be achieved.

District heating is expected to play an essential role in the cost-effective decarbonisation strategy of many countries. Resource-optimised management of district heating networks depends on a wide range of factors, including demand forecasting, operational flexibility, and increasingly volatile market conditions. However, traditional operations often still rely on static models and rather simple heuristics, while holistic optimisation requires dynamic cross-domain interoperability to allow the consideration of all these factors. This paper demonstrates a proof-of-concept for a knowledge graph based optimisation problem to minimise total heat generation cost for a district heating provider. The optimisation follows a hierarchical approach based on a merit-order principle and is embedded in a model predictive control framework to allow the system to incorporate most recent information and react to disturbances promptly. A detailed sensitivity study is conducted to identify key model parameters and assess the impact of anticipated changes in regulation and market conditions. Simulation-based optimisation is used to determine the short-term heat generation mix based on data-driven gas consumption models and day-ahead forecasts for the network’s energy demand and grid temperatures. Seasonal autoregressive integrated moving average models with exogenous predictor variables are found to be sufficiently accurate and precise. The effectiveness of the approach is demonstrated for a case study of an existing heating network of a midsize town in Germany, where a reduction of approximately 20% and 40% compared to baseline operational data is obtained for operating cost and CO 2 emissions, respectively.

Urban water systems can be highly energy intensive. Yet, they are also excellent candidates for demand-response since they possess readily controllable operations and large amounts of storage. However, real-worlds trials have delivered mixed results, which implies the need to understand the underlying mechanisms of flexibility in water operations. This paper studies the potential to shift energy demand in an urban water system located in the River Thames basin, a region encompassing the city of London, England. Results show that the system could theoretically shift up to 20.1% of its annual energy demand for pumping (2.1 GWh), saving the local water utility around £5.6 million in electricity costs. However, the water system’s flexibility is shown to be highly variable due to the variability in water demands and electricity prices, in-turn affecting the financial returns from demand-response. Sensitivity analysis reveals that factors such as the seasonal reservoir control strategies and total storage capacity are key determinants of system flexibility. A relationship between water storage capacity and flexibility is also derived, which could be used to estimate water sector flexibility in other regions.

The multi-energy coupling characteristic of integrated energy system helps to promote energy efficiency improvement and facilitate renewable energy integration. However, the growing complexities from multi-energy coupling and the increasing uncertainties of renewable generations are both threatening the synergistic operation. It is imperative to fully exploit the flexibility of integrated energy system for operating reliability and economy. In view of the essential similarities from the slow dynamics of gas and thermal systems, a unified gas and thermal inertia model for integrated energy system is proposed in this paper for flexibility excavation and optimal allocation. First, gas and thermal inertia is uniformly defined considering inertial characteristics. Furthermore, a method for unified modelling of gas and thermal inertia is accurately illustrated based on the dynamic model of gas and thermal system. To prove the validity of the unified model, a multitype reserve procurement scheme considering gas and thermal inertia is proposed. A typical community-scale integrated energy system is employed as a test system. The simulation results demonstrate the potential ability of gas and thermal inertia to improve the operational flexibility, reliability and economy of integrated energy system.

Battery storage provides flexibility to the power system and supports the increased integration of renewable energy sources. Distributed battery storage systems that are behind the meter are operated by their local owners, whose objectives may not align with those of the national power system. This paper presents a Bilevel optimisation approach to investigate the exchange of electricity between distributed battery storage and the national power system. The independent operating objectives of the battery storage systems are explicitly considered to assess their impact on the operation of the national power system. A comparison with a Centralised optimisation approach, that assumes a single objective function for the whole system, shows that the Bilevel optimisation approach captures the independencies of distributed battery storage objectives, while accounting for its interactions with the wider power system. The results show that the Centralised optimisation approach tends to overestimate the value of distributed battery storage for the power system. The results also highlight the influence of the retail contract structure in maximising the value of distributed battery storage for the national power system.

In this paper, we apply flexibility-based operational planning method to microgrid (MG) unit commitment (UC). The problem is formulated based on model predictive control (MPC) paradigm. Conventionally, such paradigm requires the online solution of a mixed-integer optimization problem, which faces difficulties in practical field implementations in a low-power computing microgrid controller. The explicit model predictive control (EMPC) can address this problem of MPC by computing the control laws of MPC in an explicit form offline to enable fast online computation. However, the complexity of the explicit control laws usually grows exponentially with the dimension of the problem, which hinders the application of EMPC in larger systems. In this paper, we integrate learning and EMPC to develop a computationally efficient and rigorous approach for implementing flexibility-based unit commitment paradigms in a microgrid controller with limited computational power. The computational complexity of the proposed approach can be adjusted to meet the hardware limitation of any given microgrid controller, while preserving as much as possible the optimality of the full-fidelity EMPC. This overcomes the drawbacks of traditional EMPC. Moreover, compared to the existing learning-based methods for accelerating optimization algorithms, the proposed approach is able to handle the variables and constraints of the original unit commitment problem systematically, which guarantees the feasibility of its output unit commitment schedules. We conduct case studies to demonstrate the effectiveness of the proposed approach.

This paper presents a predictive control scheme for coordinating a set of heterogeneous and complementary resources at different time scales for the provision of ancillary services. In particular, we combine building thermodynamics (slow), and energy storage systems (fast resources) to augment the flexibility that can be provided to the grid compared to the flexibility that any of these resources can provide individually. A multi-level control scheme based on data-based robust optimization methods is developed that enables heterogeneous resources at different time scales (slow and fast) to provide fast regulation services, especially a secondary frequency control (SFC) service. A data-based predictor is developed to forecast the future regulation signal, and is used to improve the performance of the controller in real-time operation. The proposed control method is used to conduct experiments, for nine consecutive days, demonstrating the provision of SFC service fully complying to the Swiss regulations, using a controllable building HVAC system on the EPFL campus and a grid connected energy storage system. The experimental results show that optimally combining such slow and fast resources can significantly augment the flexibility that can be provided to the grid. Moreover, by providing SFC service, the building can reduce its operational costs by up to 46% on average while maintaining a high level of occupant comfort. To the best of author’s knowledge this work is the first experimental demonstration of coordinating heterogeneous demand-response to provide SFC service.

The operation of community integrated energy systems (CIESs) is challenging because it is necessary to couple energy production, conversion and consumption on the end-user side. Multiple energy demands need to be fed simultaneously with the goal of economy and reliability. This paper formulates the optimal scheduling problem based on a real community integrated energy system in China. The system has multiple chillers (i.e. three ground source heat pumps, two conventional water-cooled chillers and two double-duty chillers) and two types of thermal storage devices (i.e. two cold water tanks and an ice-storage tank). Based on detailed device modeling, a community integrated energy system operation strategy considering unit commitment is proposed, and then is transformed into a mixed-integer linear programming model by linearization of nonlinear items. Case studies are conducted based on the data for a typical day in summer. The results show that the proposed strategy can utilize the flexibility of the energy storage devices and realize an economic and reliable operation of the community integrated energy system by coordinating various energy devices. The strategy can also reduce the startup/shutdown frequency of chillers significantly.

Microgrids are emerging to coordinate distributed energy resources and locally increase reliability to expected events and resilience to extreme events. Furthermore, by deploying their inherent flexibility, grid-connected microgrids are capable to provide different services at the system-level too. However, these are often assessed independently and a comprehensive integrated framework that can assess benefits for the whole power system is missing. In this outlook, this paper introduces a system-level assessment framework based on the concept of different duration’s reserve services that can be provided by microgrids to the main electricity grid in response to both credible (reliability-oriented) and extreme, possibly unforeseen (resilience-oriented) contingencies. Probabilistic capacity tables accounting for different sources of uncertainty related to both microgrids’ operation and occurrences of unfavorable events are built to assess the microgrids’ potential capacity contribution to a particular reserve service. Case studies based on representative microgrids and a British test system clearly illustrate and quantify how aggregation of microgrids could provide significant contribution to both short-term reliability and longer-duration resilience services far beyond the simple summation of the individual contributions, thus demonstrating a clear synergic effect, as well as the key role played by different forms of energy storage. The proposed framework can assist policy makers and regulators on the strategic role of microgrids for energy system planning and policy developments, including design of ancillary services markets not only to enhance system reliability but also resilience.

The interdependency across natural gas, power and heating systems is increasingly tightened due to the wide development of cogeneration plants and electrified heating facilities. Multi-energy integration is a prevalent trend and the energy hub, which acts as an intermediary agent between providers and consumers, is expected to play a central role in allocating energy resources more efficiently. However, uncertainties originating from multiple kinds of energy demands challenge the operation of energy hubs and may compromise system efficiency. Energy trading and sharing among individual hubs offer a unique opportunity to increase system flexibility and reduce the cost under demand uncertainty. In this paper, three quintessential schemes for organizing a cluster of energy hubs at demand side, i.e., individual, sharing market, and aggregation, are studied under a stochastic framework with probabilistic load forecasts. First, we perform theoretical analysis and compare their economic efficiencies from a maximum-utility (or minimum-cost) perspective. Utility curves of respective schemes are given, and several important phenomena are revealed from the economic analysis. Then we discuss the concrete decision-making models of energy hubs under the three schemes, taking into account the change of electricity price in response to the total demand, which give rise to bilevel optimization problems and are technically transformed into mixed-integer linear programs. Finally, we conduct numerical experiments, which validate the theoretical outcomes, and reveal that the sharing scheme can achieve nearly optimal efficiency without a central organizer, and hence appears to be a promising direction for future multi-energy systems.

This paper proposes a realistic demand side management mechanism in an urban district heating network (DHN) to improve system efficiency and manage congestion issues. Comprehensive models including the circulating pump, the distribution network, the building space heating (SH) and domestic hot water (DHW) demand were employed to support day-ahead hourly energy schedule optimization for district heating substations. Flexibility in both SH and DHW were fully exploited and the impacts of both weekly pattern and building type were modelled and identified in detail. The energy consumption scheduling problem was formulated for both the individual substations and the district heating operator. Three main features were considered in the formulation: user comfort, the heat market and network congestion. A case study was performed based on a representative urban DHN with a 3.5 MW peak thermal load including both residential and commercial buildings. Results show an up to 11 % reduction of energy costs. A sensitivity analysis was conducted which provides decision makers with insights into how sensitive the optimum solution is to any changes in energy, user comfort or pumping costs.

Deployment of distributed generation technologies, especially solar photovoltaic, have turned regular consumers into active contributors to the local supply of electricity. This development along with the digitalisation of power distribution grids (smart grids) is setting the scene to a new paradigm: peer-to-peer electricity trading. The design of the features and rules on how to sell or buy electricity locally, however, is in its early stages for microgrids or small communities. Market design research focuses predominantly on established electricity markets and not so much on incentivising local trading. This is partially because concepts of local markets carry distinct features: the diversity and characteristics of distributed generation, the specific rules for local electricity prices, and the role of digitalisation tools to facilitate peer-to-peer trade (e.g. Blockchain). As different local or peer-to-peer energy trading schemes have emerged recently, this paper proposes two market designs centred on the role of electricity storage. That is, we focus on the following questions: What is the value of prosumer batteries in P2P trade?; What market features do battery system configurations need?; and What electricity market design will open the economical potential of end-user batteries? To address these questions, we implement an optimisation model to represent the peer-to-peer interactions in the presence of storage for a small community in London, United Kingdom. We investigate the contribution of batteries located at the customer level versus a central battery shared by the community. Results show that the combined features of trade and flexibility from storage produce savings of up to 31% for the end-users. More than half of the savings comes from cooperation and trading in the community, while the rest is due to battery’s flexibility in balancing supplydemand operations. © 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 30.8.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Residential heat pumps are one of the most resource-efficient means for space heating, and their market shares are expected to rise substantially over the next few decades. Through installation of a thermal energy storage, the operation of a heat pump is – to a certain extent – decoupled from heat demand. Such flexibility is frequently claimed to bear a high potential for demand-side management. This study investigates the operational cost savings achievable through the use of such a flexibility option. To this end, a dynamic model of a coupled building and heating system is developed, and several economic model-predictive control strategies for heat-pump operation are derived. These strategies exploit real-time prices and the flexibility of the thermal capacity of the building and/or heat storage. The proposed algorithms are applied using perfect and imperfect foresight. Furthermore, different variations of real-time prices along with several building and storage configurations are investigated. The resulting operational cost savings are contrasted with different paths of investment-cost developments. Results of this investigation demonstrate that investments in flexibility measures (smart-home equipment, thermal storage capacity, etc.) are, for the most part, economically unviable. However, it is also realized that economic feasibility depends greatly on the technical set-up and economic conditions. This study demonstrates that the potential of flexible heat-pump operation is frequently overstated. On the other hand, configurations for which overall system-cost savings could be deemed achievable are also identified.

The operational flexibility of traditional coal-fired power generators has played crucial parts in many countries given the continuous integration of intermittent and fluctuant renewable power resources in their power system. This study is aimed at improving the short-term operational flexibility of thermal power units. The transient simulation models of a 660 MW supercritical coal-fired power system are developed by using the GSE software, and the six measures of regulating thermal system configuration, which can rapidly activate thermal storages in thermal systems, are introduced and compared. The greatest potential mode in each measure of regulating thermal system configuration is obtained on the basis of the utilization degree of the internal thermal storages. The dynamic characteristics of the thermal parameters of the selected measures are compared, and the results reveal that the measures of regulating extraction steam from low-pressure heaters has less negative effects on the live and reheat steam parameters but greater negative effects on the deaerator and condenser parameters than the measures of regulating extraction steam from high-pressure heaters. Moreover, the performance indexes of operational flexibility are also evaluated for all the measures of regulating thermal system configuration. Among five measures applied in the power-up process, the values of power ramp rate are from 1.26% to 6.31% of rated power per minute, the values of power capacity are from 12.87 to 48.35 MW, and the values of energy capacity during the regulating time of 120 s are 1009.73 to 5048.93 MJ. However, the measure of condensate water increasing of low-pressure heaters is an only way suitable for the power-down regulation process, and the value of power ramp rate is 0.61% of rated power per minute, the power capacity is −5.65 MW, and the energy capacity is −483.84 MJ. The best regulating measures are obtained on the basis of the different perspectives of operational flexibility. The findings of this study can help determine the best operational control strategies in different situations.

The inherent stochastic nature of wind power requires additional flexibility during power system operation. Traditionally, conventional generation is the only option to provide the required flexibi ...

This study contributes to the existing literature on optimal carbon mitigation policy by quantifying the impacts of various sectoral coverage options for the emissions trading systems (ETS) used to achieve China’s Intended Nationally Determined Contribution (INDC) targets for the Paris Agreement on climate change. The CHEER model, a computable general equilibrium (CGE) model of China with detailed representation of electricity and other energy intensive sectors, as well as a complete CO 2 emissions accounting module and carbon market, is used in this study. Results show several important findings. First, China’s INDC targets can be achieved through an economy-wide ETS at an economic cost of 2.1% of real GDP by 2030. Second, including only the eight sectors proposed for initial implementation of the ETS in China is likely to result in a much larger mitigation cost than the economy-wide approach, estimated to be as high as 10.5% of 2030 real GDP. Thirdly, this study further indicates that the mitigation costs can be reduced to 3.3% of real GDP in 2030 if other energy-intensive sectors, accounting for additional 24.8% of total emissions, are included in the ETS. As a result, not all sectors are required to get close to the first-best mitigation option so long as critical sectors are not excluded. In addition, the temporal dimension of mitigation costs and air pollution co-benefits under different sectoral schemes of China’s ETS gives policy-makers a degree of short-run flexibility in terms of phasing in additional industries over time.

The large penetration rate of renewable energy sources leads to challenges in planning and controlling the energy production, transmission, and distribution in power systems. A potential solution is found in a paradigm shift from traditional supply control to demand control. To address such changes, a first step lays in a formal and robust characterization of the energy flexibility on the demand side. The most common way to characterize the energy flexibility is by considering it as a static function at every time instant. The validity of this approach is questionable because energy-based systems are never at steady-state. Therefore, in this paper, a novel methodology to characterize the energy flexibility as a dynamic function is proposed, which is titled as the Flexibility Function. The Flexibility Function brings new possibilities for enabling the grid operators or other operators to control the demand through the use of penalty signals (e.g., price, CO2, etc.). For instance, CO2-based controllers can be used to accelerate the transition to a fossil-free society. Contrary to previous static approaches to quantify Energy Flexibility, the dynamic nature of the Flexibility Function enables a Flexibility Index, which describes to which extent a building is able to respond to the grid’s need for flexibility. In order to validate the proposed methodologies, a case study is presented, demonstrating how different Flexibility Functions enable the utilization of the flexibility in different types of buildings, which are integrated with renewable energies. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)