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This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called ``preparation range'' to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.

Series-cascaded Microgrids (MGs) are gaining increasing popularity because of their control flexibility and ability to synthesise high voltage levels from low power rated modules. Several control schemes have been reported for autonomous power sharing in series cascaded microgrids that incorporate dispatchable Distributed Generators (DGs) only. In contrast, this paper develops a series cascaded microgrid formed by dispatchable and non dispatchable DGs. Achieving power balance as well as voltage and frequency regulation in this microgrid configuration is a challenging task. This work proposes a decentralised master slave control scheme that regulates voltage and frequency and maintains power balance. The proposed scheme ensures maximum utilisation of non dispatchables, incorporates autonomous power curtailment under light load conditions, and offers system reliability comparable to the traditional parallel structure. The proposed microgrid configuration and control scheme are thoroughly validated via simulations and experiments under various operating scenarios.

The pricing scheme plays a vital role in the demand-side management program. It allows utility, indirect control over the users energy consumption. This article proposes a new pricing structure, named as a real-time variable peak pricing scheme with a self-inbuilt feature to capture the benefit of both the existing pricing scheme, real-time pricing (RTP), and time of use (TOU), by switching its behavior between RTP and TOU as per the requirements. A home energy management model with comprehensive modeling of home appliances together with distributed storage and dispatchable generation is considered for testing the impact of the proposed pricing scheme and compared to existing RTP and TOU. The privacy and comfort of users are taken care of in the system modeling. An aggregative game approach is used to capture the interaction between users and utility. The conditions for the existence of Nash equilibrium and convergence of the proposed algorithm is investigated for all three pricing schemes considering residential users. The analysis shows the effectiveness of the proposed pricing scheme compare to the existing pricing schemes in terms of consumers energy bill and system peak reduction, complexity, and incentive to active consumers to participate in demand-side management programs.

Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ahead and intraday dispatch model for maximizing the profit of an Integrated Biomass-Concentrated Solar (IBCS) system considering the synergies arising from their coupled operation. To sensibly capture uncertainty and decision sequence of real-life electricity markets, a two-stage stochastic structure is proposed, while the solar-related uncertainty is involved using Information Gap Decision Theory (IGDT). The model is complemented with a novel multi-objective architecture based on the compound of IGDT and Conditional Value-at-Risk (CVaR), which allows handling risk exposure to both stochastic and IGDT inputs. The Pareto strategies in the multi-objective model are extracted through an expanded form of the -constraint method, whereas a posteriori approach based upon the out-of-sample assessment is applied to derive the optimal dispatch pattern among the generated Pareto strategies. The simulation results demonstrate that: 1) the proposed integrated dispatch model achieves substantial profitability, and 2) the performance of the suggested CVaR-IGDT model is superior to conventional approaches.

This paper studies price-based residential demand response management (PB-RDRM) in smart grids, in which non-dispatchable and dispatchable loads (including general loads and plug-in electric vehicles (PEVs)) are both involved. The PB-RDRM is composed of a bi-level optimization problem, in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company (UC) by selecting optimal retail prices (RPs), while the lower-level demand response (DR) problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior. The challenges here are mainly two-fold: 1) the uncertainty of energy consumption and RPs; 2) the flexible PEVs' temporally coupled constraints, which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM. To address these challenges, we first model the dynamic retail pricing problem as a Markovian decision process (MDP), and then employ a model-free reinforcement learning (RL) algorithm to learn the optimal dynamic RPs of UC according to the loads' responses. Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches (i.e., distributed dual decomposition-based (DDB) method and distributed primal-dual interior (PDI)-based method), which require exact load and electricity price models. The comparison results show that, compared with the benchmark solutions, our proposed algorithm can not only adaptively decide the RPs through on-line learning processes, but also achieve larger social welfare within an unknown electricity market environment.

Hydrogen fueling stations (HFSs) with onsite hydrogen production systems, which are usually composed of electrolyzers, hydrogen storage tanks and fuel cells, not only supply hydrogen for hydrogen-powered vehicles but also serve as a dispatchable technology that can bid in electricity markets. Except participating in energy market, joining in reserve market can compensate the cost in energy market and increase the total revenue of HFS. This paper proposes a multi-stage stochastic programming model to find the optimal offering strategy of the HFS in energy, reserve markets taking into account a series of uncertainties: day-ahead price, secondary reserve price, system imbalance price and hydrogen demand. Nonanticipativity constraints are employed to guarantee the decisions are made according to the realized uncertainty information up to the present stage. Compared with traditional stochastic programming model, the proposed model adequately considers the sequential bidding decisions with the gradual revealing of the uncertainty over time. Numerical experiments based on one case study indicate that the participation of reserve market greatly increase the revenue of HFS. In addition, the proposed multi-stage stochastic programming model is effective in characterizing the sequential decision.

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

This article presents a distributed consensus-based successive convex approximation (DSCA) algorithm to solve nonconvex nondifferentiable economic dispatch (ED) problems. The ED model formulated incorporates generation constraints, valve-point effects, and multiple fuel types. A perturbation technique enables the proposed DSCA to tackle such a nondifferentiable and nonconvex optimization, which paves the way to solving more complicated optimization problems that occur in practical applications. The local generation constraint is taken care by a local surrogate convex optimization directly. The global equality constraint is handled based on a consensus protocol, where the local generation–demand mismatch among all dispatchable generators (DGs) is shared in a distributed manner. As a result, the power distribution of DGs is updated, and the generation cost is minimized. Several case studies show that the proposed DSCA algorithm can achieve superior ED solutions and computational efficiency over existing nonconvex optimization algorithms.

With the increasing effects of climate change and high costs of energy, many rural Alaska communities are working to implement local alternative energy solutions to improve energy security. Integrating renewable energy systems can reduce reliance on fossil fuels and subsequently improve food, energy, and water (FEW) security. In this study, wind energy modeling techniques using local airport meteorological data were convolved with community loads to determine the most cost-effective combinations of wind turbine technology and dispatchable loads for improving FEW security in a southwestern Alaska village. This approach is different from wind assessments that exclusively analyze wind resources. A 100 kW wind turbine was determined to be suitable for the community, resulting in a capacity factor of 16.7% and levelized cost of energy (LCOE) of $1.15/kWh, with diminishing returns for higher wind turbine capacity. The results from the dispatchability study indicated that dispatchable loads could handle the intermittency of the wind resource with up to 86% of their annual load met. More work is needed to understand the impact of integrating and scheduling dispatchable loads into the grid in practice.

High penetration of renewable energy generation (REG) in the distribution system increases both the power uncertainty at a given interval and the power variation between two intervals. Reserve markets addressing power uncertainty have been widely investigated. However, there is a lack of market mechanisms regarding the power variation of the load and REGs. This paper thus defines a planned ramping (PR) product to follow the net load variation and extends the local energy market to including the trading of PR products. Players are economically compensated for their PR products. Bidding models of dispatchable generators and flexible load aggregators in the joint market are investigated. To solve the market problem in polynomial time, a distributed market clearing method is developed based on the ADMM algorithm. The joint market is tested on a modified IEEE 33-bus system. It verifies that introducing the PR market can encourage flexible loads to provide more PR service to accommodate the net load variation. As such, the ramping cost of dispatchable generators is reduced by 29.09% in the test case. The planned energy curtailment from REG is also reduced. The computational efficiency of the proposed distributed clearing method is validated by comparing it with a centralized method.

Onshore wind electricity generation is key to mitigating greenhouse gas emissions.​ Poorly sited wind farms degrade high carbon soils and habitats, diminishing overall emission reductions. We explore the viability of the Scottish Government’s renewable energy plan with respect to land use, natural capital and low carbon storage. With avoidance of sensitive peatlands a main consideration, six constraining factors were combined to determine areas of least habitat and soil sensitivity to onshore wind development in Scotland. Currently, 14 out of 21 terrestrial habitats have been impacted by installation of 389 onshore wind sites. Accounting for 73% of the total area, Coniferous Woodland, Acid Grassland, Bog, and Heather Grassland have been the largest habitats impacted. The most common soils of the least sensitive areas available for installation are brown earth and podzols, and construction of new wind farms on environmentally sensitive areas can be minimised by targeting relatively disturbed habitats such as improved grasslands. Scotland has a potential of 2.75 Mha of relatively low sensitive land, the largest areas sited in the Highlands, Dumfries and Galloway and Aberdeenshire. Additional to current installed capacity (13.9 GW), Scotland would require 6.6 GW of installed onshore wind capacity to function without nuclear energy generation and 464 GWh additional storage capacity (provided by 8.2 GW wind capacity). This equates to an installed and additional total of 346.676 ha required for wind electricity generation, potentially satisfied by shared land use with 23% of Scottish improved grasslands. Scotland has the available land area to achieve the Scottish Government’s policy to move towards carbon-neutral, nuclear-free electricity generation through the use of renewables alone. Questions remain on which source of low carbon dispatchable (immediately accessible) energy to use in the case of a several day wind lull.

Large-scale carbon capture, utilization, and storage (CCUS) requires development of critical infrastructure to connect capture locations to geological storage sites. Here, we investigate what government policies would be required to make the development of CO2 pipelines and large-scale CCUS in the power sector economically viable. We focus on the transition from conventional coal to non-CO2-emitting natural gas-fired Allam-cycle power with CCUS and study a system in which 156 Allam-cycle power generators representing 100 GW of capacity send their captured CO2 emissions to three geological storage locations in the central United States through 7500 miles of new pipeline. Enabling policies for this system include low-interest government loans of approximately $20 billion for pipeline construction and an extended 20-year Section 45Q tax credit, or similar longer-term carbon price incentive. Additional policy support will be needed to enable initial construction of pipelines and early-mover power generators, such as cost-sharing, governments assuming future demand risk, or increased subsidies to early movers. The proposed system will provide reliable, dispatchable, flexible zero-emission power generation, complementing the intermittent generation by renewables in a decarbonized U.S. power sector. The proposed pipeline network could also connect into future regional infrastructure networks and facilitate large-scale carbon management.

Dispatchable renewable generation is essential for 100% renewables power system. It is in the interest of both power system operators and customers since it reduces the need for flexibility and reserve. To this end, battery energy storage systems (BESS) are proposed for integration in the renewable power plant. This paper presents the optimal dispatch unit for a dispatchable hybrid solar‐wind power plant with BESS framework. It achieves optimal dispatchable renewable generation (from dispatchable hybrid renewable (solar‐wind) power plant with BESS, DHRB, operator perspective), subject to operational limits, by exploiting the synergy of wind and solar energy and combining it with storage capability of BESS using two different operation strategies, maximisation of revenues and maximisation of renewables harvesting. A continuous BESS degradation model is incorporated in the proposed rolling‐algorithm‐based‐optimal dispatch unit to improve the accuracy of results. The applicability of the proposed methodology and the performance of the operation strategies are demonstrated using a case study and the operation strategies are compared. Further, the effect of BESS size on its degradation and dispatchable power in a hybrid solar‐wind power plant with BESS are investigated through BESS lifetime. An indicative economic analysis is carried out to provide ground for the extra investment on and sizing of BESS for a dispatchable hybrid renewable power plant.

This article introduces a dual-input single-output dc–dc power electronic converter topology to interface two independent sources into a common output. The proposed converter is denoted as integrated dual-dc boost converter (IDDBC), and this topology provides step-up conversions for both inputs using three controllable switches. Due to its architecture, IDDBC can be adapted to provide uninterrupted power (realizing a dispatchable system) even if any one of the two input sources are unavailable or has significant output power variation. The steady-state analysis, modeling, and design of the converter are presented, and a pulsewidth-modulation control scheme is proposed. The closed-loop control system for the converter consists of voltage-mode control for output and current-mode control for one-source input current. The behavior of converter operation is validated through experimentation on a digital-signal-processor-based laboratory prototype.

The terms generation, transmission, and distribution are the categories under which operations and planning of electric power systems have traditionally been discussed. Notable by their absence are the facilities in which the electricity that is generated, transmitted, and distributed is consumed-homes, buildings, and industrial plants. This exclusion was logical when generation was centralized and dispatchable; the supplydemand balance could be maintained through actions at generators. Variations in consumption, whether predictable in the short or long term or the result of unforeseen contingencies, could be addressed by increasing or decreasing kilowatts produced.

Cellular base stations (BSs) are equipped with backup batteries to obtain the uninterruptible power supply (UPS) and maintain the power supply reliability. While maintaining the reliability, the backup batteries of 5G BSs have some spare capacity over time due to the traffic-sensitive characteristic of 5G BS electricity load. Therefore, the spare capacity is dispatchable and can be used as flexibility resources for power systems. This paper evaluates the dispatchable capacity of the BS backup batteries in distribution networks and illustrates how it can be utilized in power systems. The BS reliability model is first established considering potential distribution network interruptions and the effects of backup batteries. Then, the analytical formula of the BS availability index is derived with respect to batteries’ backup duration. The dispatchable capacity of BS backup batteries is evaluated in different distribution networks and with differing communication load levels. Furthermore, a potential application, daily operation optimization, is illustrated. Case studies show that the proposed methodology can effectively evaluate the dispatchable capacity and that dispatching the backup batteries can reduce 5G BS electricity bills while satisfying the reliability requirement.

This article proposes a dispatchable droop control method for multiple distributed generators (DGs), which can be applied in islanded ac microgrids. The proposed method utilizes first-order inertia elements to achieve pseudo hierarchical control so that the DG can share the load automatically on a smaller time scale and obey the dispatch order on a larger time scale. On one hand, the proposed method contains active power regulation and frequency restoration control simultaneously. On the other hand, it has either the reactive power regulation control or the voltage control for different situations. The proposed method is still applicable even when the given power control references are infeasible. The effectiveness of the proposed method is verified by the corresponding simulation based on MATLAB/Simulink.

In this paper we consider the aggregation of common convex and non-convex individual Demand Response (DR) models for responsive loads, and apply the Shapley-Folkman (SF) lemma to show that such an aggregate is approximately convex in its action space and cost, and strictly convex under mild conditions. We then discuss how reduced order convex aggregate models can be passed on to System Operators for inclusion in economic and operational models, including a novel polytope approximation as well as an ellipsoidal model derived in a distributed fashion. Numerical simulations confirm our application of the Shapley-Folkman lemma and show that the new reduced order models outperform conventional virtual generator approximations.

Markets are changing as the result of (1) the addition of variable wind and solar that causes highly volatile electricity prices and (2) the goal of a low-carbon economy. These changes require econ...

While it is widely known that the solar resource is sufficient to meet the world’s energy demand many times over, the questions of where and how much to deploy in a realistic context do not have such clear-cut answers. The objective of this paper is to address and inform these questions in a context where solar (embodied by PV) would be applied locally to firmly meet the bulk of energy demand from regional economies. Sensible answers are important in light of growing societal mandates to displace carbon-based energy resources. We aim to provide realistic and comprehensive numbers that can effectively inform planning decisions at local and regional levels. We focus on the continental United States (CONUS) and develop state-specific PV deployment requirements informed by: • A full accounting of states’ energy requirements from the electric sector as well as [to be] electrified transportation and building (HVAC) sectors. • Positing that the bulk of this demand will be met within each state with an optimized blend of PV and wind with a small residual allowance for dispatchable generation – an optimized blend of resources estimated from our recent grid-specific investigations in diverse climatic and socio-economic environments. • A recognition that electrical demand must be met firmly, hence that intermittent renewables must be transformed into firm, effectively dispatchable resources available 24/365 to maintain a stable electrical grid. • A recognition that the least-cost solution to achieve this intermittent-to-firm transformation implies overbuilding and proactively curtailing these resources - we apply herein an estimated overbuild amount estimated from our recent investigations in diverse environments. • Not accounting for likely energy efficiency improvements in any of the three considered demand sectors. Therefore, the numbers developed can be considered to be conservatively high. From these requirements, we explore PV deployment options using two distinct approaches: a top-down approach assigning a fraction of plausible deployment potential to land use classes as defined by the US geological Survey, and a bottom-up approach starting from end-use applications prospectively amenable to PV deployment without change of function. In addition, we provide readers with an online interactive capability to modify fractional land use selections applied in this article and further investigate state-specific potentials. We conclude that a majority of the three-sector firm power requirements could be met economically and firmly by locally-deployed PV resources with ample deployment room to grow, even in the most densely populated northeastern states. This conclusion applies even before considering energy efficiency improvements or tapping other renewable resources that may be available locally (e.g., hydropower).

Summary Buildings use 75% of US electricity; therefore, improving the efficiency and flexibility of building operations could provide significant value to the rapidly changing electricity system. Here, we estimate the technical potential near- and long-term impacts of best-available building efficiency and flexibility measures on annual electricity use and hourly demand across the contiguous United States. Co-deployment of building efficiency and flexibility avoids up to 742 TWh of annual electricity use and 181 GW of daily net peak load in 2030, rising to 800 TWh and 208 GW by 2050; at least 59 GW and 69 GW of the peak reductions are dispatchable. Implementing efficiency measures alongside flexibility measures reduces the potential for off-peak load increases, underscoring limitations on load shifting in efficient buildings. Overall, however, we find a substantial building-grid resource that could reduce future fossil-fired generation needs while also reducing dependence on energy storage with increasing variable renewable energy penetration.

This paper proposes a resilient and secure configuration for coastal distribution grids by integrating the security constraint unit commitment (SCUC) and mobile marine microgrids (MMMGs). In the proposed configuration, MMMGs can be connected to the coastal distribution grids in both normal and post-disaster operations. It is assumed that both MMMGs and SCUC networks include both dispatchable (e.g., gas turbines and diesel generators) and nondispatchable generators (e.g., photovoltaics and wind turbines). The proposed problem consists of realistic formulations that seek to minimize the total MMMGs and SCUC operation costs, while maximizing distribution grid resiliency. A heuristic technique, known as the collective decision optimization algorithm, is employed to address the complexity and nonlinearity of the formulated problem. Moreover, the unscented transform technique is adopted to model the uncertainties associated with renewable energy sources output and load demand. To show the effectiveness and merits of the proposed configuration, the IEEE 69-bus distribution network is selected and tested for both normal and post-disaster operations.

With the popularity of plug-in electric vehicles (EVs) and the development of the vehicle to grid (V2G) technology, EVs can be aggregated and behave as a controllable storage system via the Internet of Things. However, it remains an open question as to how large-scale EVs can be effectively integrated into the system-level operation. In this article, we propose a dispatchable region formation approach of EV aggregation to capture its available flexibility in microgrid (MG) bidding. The dispatchable region of EV aggregation describes the feasible operation strategy as a single entity, characterized by its power and cumulative energy limits. Instead of scheduling an individual EV, the dispatchable region of large-scale EVs enables the MG operator to directly schedule the EV aggregation toward market revenue maximization. The MG bidding strategy is formulated as a risk-constrained stochastic programming, which maximizes day-ahead market profits considering real-time imbalance settlement in a dual-pricing market. Case studies based on real-world datasets demonstrate that the proposed dispatchable region approach in MG biding can significantly improve both computation efficiency and forecasting accuracy.

Deploying distributed renewable energy at the demand side is an important measure to implement a sustainable society. However, the massive small solar and wind generation units are beyond the control of a central operator. To encourage users to participate in energy management and reduce the dependence on dispatchable resources, a peer-to-peer energy sharing scheme is proposed which releases the flexibility at the demand side. Every user makes decision individually considering only local constraints; the microgrid operator announces the sharing prices subjective to the coupling constraints without knowing users' local constraints. This can help protect privacy. We prove that the proposed mechanism can achieve the same disutility and flexibility as centralized dispatch, and develop an effective modified best response based algorithm for reaching the market equilibrium. The concept of absorbable region is presented to measure the operating flexibility under the proposed energy sharing mechanism. A linear programming based polyhedral projection algorithm is developed to compute that region. Case studies validate the theoretical results and show that the proposed method is scalable., 12 pages, 16 figures, accepted by IEEE Transactions on Smart Grid

The increased penetration of renewable energy sources into the power system has resulted in a need for dispatchable control for the inverters. Dispatchable generation enables intelligent use of power sources thus enhancing the system's efficiency, reliability and reducing the stress on the distribution lines. In this article, a dispatchable control for virtual oscillator controlled-inverters is presented to achieve simultaneous regulation of both the active and reactive power in a single-phase system of $\boldsymbol{m}$ -controlled and $\boldsymbol{n}$ -uncontrolled inverters. Further, a set of security constraints is derived to determine the achievable power set-points for each controlled inverter present in the system. The dispatchable controller parameters corresponding to the desired power set-point can be determined using the presented control laws. A technique to design the control parameters and line impedance for the uncontrolled inverters is presented to enable optimal power sharing proportional to their power ratings. Simulation and experimental results are presented for the power regulation of multiple virtual oscillator controlled-inverters connected to a load through respective line impedances. The proposed dispatchable control strategy effectively regulates both the active and reactive power of the controlled inverters for a number of cases and in a wide operating range.

The paper proposes optimal placement of dispatchable distributed generation units along with solar photovoltaic as distributed generator (DG) and subsequent optimal capacity operation of the dispatchable DG units in presence of solar photovoltaic (SPV) generation, i.e. when SPV generation is available. It is proposed here that these dispatchable DG units when operated at co-ordinated optimal capacity, in presence of SPV generation, result in further loss reduction without an additional DG unit installation The additional loss reduction so obtained results in more loss reduction and subsequent saving in loss cost. In this paper, a methodology is presented for optimal placement of solar photovoltaic distributed generation (SPVDG) in presence of other optimally placed dispatchable DG units with the assumption that the dispatchable DGs units were present in the distribution system before insertion of optimal SPVDG. An effective methodology is presented here for optimal operation of dispatchable DGs in presence of SPV generation. It is proposed and established that DG technologies (which are dispatchable in nature), must be operated at different optimal capacities than their respective installed optimal capacity, for further loss reduction when solar generation is available. This optimal operation defers the additional DG capacity addition for system loss reduction and at the same time provides a reduction in operational cost for certain dispatchable DG types. It is proposed that in presence of SPVDG, other distributed generators must be operated at somewhat reduced optimal capacity, but at their respective optimal location, for hours, when photovoltaic generation is available. This enables a reduction in their operational cost as well.

The increasing uptake of distributed energy resources (DER) is displacing the dispatchable generators responsible for providing frequency response in our power system. To compensate for the withdrawal of such generators, the market participation of DER fleets through aggregators has been suggested in the literature. Unfortunately, these works are either limited to price-taking aggregators or neglect the distribution network. The first limitation leads to inelastic bids which do not reflect DER flexibility, while the second results in bids that do not lie within the network boundaries. To mitigate these challenges, we propose a price-elastic aggregator bidding approach together with a network conforming layer. Our approach enables aggregators to offer a range of network-secure bid bands for different prices. To preserve the independent role of each stakeholder and share the computational burden, we decompose the problem into aggregator and network subproblems, solved sequentially. Moreover, we confer aggregators the possibility to provide reactive power support to enable the network to accept greater throughput. Finally, since our approach ensures network feasibility prior to bids reaching the market, it minimizes the disruption to existing wholesale market structures. Our results on a 141-bus network shows 19% higher benefits for aggregators, compared to the inelastic bidding approach.

Concentrating solar power (CSP) with thermal energy storage (TES) presents the major advantage over solar photovoltaics of dispatchability. High thermodynamic efficiencies achieved by collecting and storing heat at higher temperatures, and recent maturing of the technology, are making molten-salt central receiver plants the preferred option for CSP. To explore potential further improvements in CSP efficiency and cost the world’s first direct absorption molten salt volumetric receiver/storage system was built at pilot scale, commissioned and monitored. In this demonstration a 100 kWth beam-down tower directs solar radiation through a final concentrator into the open aperture of a 1.94 m high and 1.25 m internal diameter tank receiver situated near the ground. The receiver tank is filled with 3,800 kg of 60–40 wt% NaNO3-KNO3 and serves as a stratified or mixed single tank thermal store that can satisfy evening peak loads or provide baseload power through the night. Compared to the parasitic loads of a conventional tower-receiver plant, the energy needed for salt transport from receiver to TES and morning preheat is negligible for this new system. The hot-spot problem of tubular receivers is eliminated and. the combined receiver/storage tank reduces component costs. In-situ initial melting was accomplished using solar energy as the primary input. Thermal stratification was maintained by daily cycling of a divider plate and occasional mixing plate actions and hot spots were never observed during several months’ operation between 250 and 500 °C. Three cycles of complete salt freezing and in-situ on-sun re-melting were tested with no operational difficulty and no discernible damage.

This chapter discusses special attributes of the specific power electronic‐based technologies to enhance system dynamics. It also discusses optimal placement of the technologies in detail. After a discussion on the application of virtual synchronous generators (VSGs) in power system stability and performance improvement, optimal placement of dispatchable inertia besides the associated realization mechanisms would be emphasized. Without the loss of generality, while distributed generations (such as wind farms), high‐voltage direct current systems, and energy storage systems (ESSs) could emulate virtual inertia, the ESSs are considered as the source of virtual inertia in this chapter. Manipulation of the inertia constant by the VSGs could enhance system dynamics and stability. This could be realized using inertia‐based control schemes. Indeed, inertia provision plays an important role in designing advanced control schemes in modern power grids. Optimal placement of virtual inertia can be studied in two perspectives: frequency stability and small‐signal stability.

Concentrated solar power (CSP) plants with thermal energy storage (TES) system are emerging as one kind of the most promising power plants in the future renewable energy system, since they can supply dispatchable and low-cost electricity with abundant but intermittent solar energy. In order to significantly reduce the levelized cost of electricity (LCOE) of the present commercial CSP plants, the next generation CSP technology with higher process temperature and energy efficiency is being developed. The TES system in the next generation CSP plants works with new TES materials at higher temperatures (> 565 °C) compared to that with the commercial nitrate salt mixtures. This paper reviews recent progress in research and development of the next generation CSP and TES technology. Emphasis is given on the advanced TES technology based on molten chloride salt mixtures such as MgCl2/NaCl/KCl which has similar thermo-physical properties as the commercial nitrate salt mixtures, higher thermal stability (> 800 °C), and lower costs (

For medium-scale dispatchable (on demand) power and fresh water generation, a concentrated solar energy powered system with thermal energy storage is an attractive option. However, currently used concentrated solar power technologies (parabolic trough collector, solar power tower, linear Fresnel reflector) use heavy and very expensive glass mirrors and receivers. Recently, a novel micro-structured polymer foil-based concentrating solar collector system, with the advantages of a low installation cost and a low operation and maintenance cost, has been proposed. This system, which generate heat by focusing sunlight like magnifying glasses, can be effectively used for applications with temperatures up to about 350 °C. Techno-economic analysis and optimization of a micro-structured polymer foil-based concentrating solar collector powered organic Rankine cycle based electricity and multi-effect distillation based fresh water generation system is presented in this paper. The proposed system has a lot of potential for the places with electricity and water crisis. The results indicate that cyclopentane is the most appropriate organic Rankine cycle working fluid for the plant, achieving a levelized cost of electricity of 0.116 €/kWhe and levelized cost of fresh water of 1.13 €/m3 for Antofagasta, Chile and 0.163 €/kWhe and 1.62 €/m3 for Cape Town, South Africa.

This article addresses the optimal bidding strategy problem of a virtual power plant (VPP) participating in the day-ahead (DA), real-time (RT) and spinning reserve (SR) markets (SRMs). The VPP comprises a number of dispatchable energy resources (DERs), renewable energy resources (RESs), energy storage systems (ESSs) and a number of customers with flexible demand. A two-stage risk-constrained stochastic problem is formulated for the VPP scheduling, where the uncertainty lies in the energy and reserve prices, RESs production, load consumption, as well as calls for reserve services. Based on this model, the VPP bidding/offering strategy in the DA market (DAM), RT market (RTM) and SRM is decided aiming to maximize the VPP profit considering both supply and demand-sides (DS) capability for providing reserve services. On the other hand, customers participate in demand response (DR) programs by using load curtailment (LC) and load shifting (LS) options as well as by providing reserve service to minimize their consumption costs. The proposed model is implemented on a test VPP and the optimal decisions are investigated in detail through a numerical study. Numerical simulations demonstrate the effectiveness of the proposed scheduling strategy and its operational advantages and the computational effectiveness.

The tradeoff between the operating cost and the reliability of microgrids imposes challenges of having cost-effective power dispatch and reliable operations. The work presented in this article optimally solves this issue in a microgrid that contains photovoltaic (PV), dispatchable generator, and storage battery. Flexible control algorithms are developed to enable high penetration of PV power in islanded microgrids such that the challenge of having a cost-effective and reliable operation can be mitigated. This is achieved by creating a power flow optimization framework that considers all variables associated with the system operation. Particle swarm optimization is utilized to effectively optimize the power flow among the integrated sources and to dynamically schedule the battery state of charge. The proposed controller of each source works to fulfill the requirements of the optimization. The energy index of reliability is employed as a direct measure to quantify the reliability level of the microgrid. The effectiveness of the proposed approach is demonstrated by simulations and validated by experimental studies.

Pakistan is identified to be one of the next-11 the top emerging economies of the world after the BRICS. This emphasizes the establishment of a competitive electricity market that can fulfil the demand of the country considering the environmental concerns. In this scenario reliability of power is something that cannot be compromised. Dispatchable power stations play a major role in balancing supply and demand; this balance is essential for maintaining the power cuts free country. All dispatchable power stations incorporate some form of storage particularly thermal or chemical (i.e. a stored fuel). Earlier dispatchable power was regarded as the generation which can start quickly and meet the peak demand requirements but arrival of renewables in power system has increased worth of its presence since it has just not to supply peak demand but also to meet during the unavailability of other renewable energy sources because of their intermittent behavior meanly at times of dark hours and slow wind speed. This study considers the viability of Concentrated Solar Thermal Power in Pakistan and thoroughly analyses several characteristics i.e., Availability of fuel, water, road and communication network, flexibility and environmental impacts of the technology for the cite of Tharparkar.

Decarbonization of the power sector is an important milestone for the achievement of ambitious GHG reduction targets. Given the intrinsic shortcomings of nuclear power and zero-emission thermal power generation, such as large investment costs and public acceptance, along with the locational limits of dispatchable renewables such as hydro and geothermal, variable renewable energies (VRE) should play an important role to decarbonize the power sector. Very high penetration of VRE, however, would require additional “integration” costs related to grid expansion, power curtailment, and power storage. In this article, focusing on a decarbonized power system in Japan in 2050, we calculated two metrics that capture the non-linear nature of the integration cost related to high VRE penetration: Average system LCOE (levelized cost of electricity) and relative marginal system LCOE. The former metric allocates the integration cost to each power source, which is divided by the adjusted power output, while the latter measures the changes in the total system cost with the substitution of two types of power sources. The results show that both the average and the relative marginal system LCOE of VRE will rise when the share of VRE rises, but the latter will rise much more sharply than the former. This suggests that the anticipated challenges for achieving very high shares of VRE may still exist even if the cost of VRE may decline rapidly in the future. As the relative marginal system LCOE of VRE can be heavily dependent on meteorological conditions, it is essential to use multi-annual data to estimate it. The metric relative marginal system LCOE can be used for the soft-linking of a detailed power sector model to an integrated assessment model, which can contribute to a better quantitative analysis of climate policies.

With the increasing integration of power electronics interfaced distributed generators, transient stability assessment of grid-connected inverters subjected to large grid disturbances is of vital importance for the secure and resilient operation of the power grid. Dispatchable virtual oscillator control (dVOC) is an emerging approach to implement nonlinear control of grid-forming inverters. Through coordinate transformation, a simple first-order nonlinear power angle dynamic equation is uncovered from the complex oscillator dynamics. Furthermore, this article proposes a concise and straightforward graphical approach to assess transient stability of dVOC using vector field on the circle. To provide a more in-depth analysis, a complete large-signal model is derived and the impact of dVOC voltage amplitude dynamics is analyzed. For comparison, transient stability of the currently prevalent droop control is also assessed using phase portraits. Salient transient stability features of dVOC and droop control during grid faults are summarized and compared. The theoretical analysis is validated by controller hardware-in-the-loop testbed using industry-grade hardware.

This article proposes an integrated optimization model for unbalanced distribution system restoration after large-scale power outages caused by extreme events. The model can coordinate the control actions of multiple types of distributed energy resources (DERs), including dispatchable distributed generators (DGs), renewable DGs (mainly wind and solar), and energy storage systems (ESSs). The model also considers topology flexibility by forming dynamic islands through reconfiguration. Besides, the optimal dispatch model of repair crews and mobile emergency generators are also proposed to leverage the restoration capabilities of existing DERs installed in the distribution systems. The integrated optimization model is linearized to be a mixed-integer linear programming form, which can be effectively solved by off-the-shelf solvers such as Cplex and Gurobi. Numerical results on IEEE 123 and 8500 node test feeders validated the effectiveness of the proposed model and highlighted the necessity of coordinating various flexible resources.

Sustainable electricity systems need renewable and dispatchable energy sources. Solar energy is an abundant source of renewable energy globally which is, though, by nature only available during the day, and especially in clear weather conditions. We compare three technology configurations able to provide dispatchable solar power at times without sunshine: Photovoltaics (PV) combined with battery (BESS) or thermal energy storage (TES) and concentrating solar power (CSP) with TES. Modeling different periods without sunshine, we find that PV+BESS is competitive for shorter storage durations while CSP+TES gains economic advantages for longer storage periods (also over PV+TES). The corresponding tipping points lie at 2–3hours (current cost), and 4–10hours if expectations on future cost developments are taken into consideration. PV+TES becomes only more competitive than CSP+TES with immense additional cost reductions of PV. Hence, there remain distinct niches for two technologies: PV+BESS for short storage durations and CSP+TES for longer ones.

This paper proposes an effective framework for optimal operation management of hybrid AC-DC microgrids incorporating both dispatchable and non-dispatchable energy sources as well as battery storage. The proposed method is constructed based on a fully distributed consensus-based structure making use of a multi-agent mechanism and alternating direction method of multipliers (ADMM). The proposed framework decomposes the microgrid into several agents within which the neighboring agents share the consensus variable (mismatch power flow) with each other. Each agent tries to minimize its own cost based on the consensus variable value received from the other agents. An augmented objective function is then formulated which is nonlinear and needs to be optimized. Due to the high nonlinearity of the objective function, a new optimization method based on crow search algorithm (CSA) is developed to find the optimal local solutions for each agent and drive the ADMM to have a mature convergence. A modified method based on crossover and mutation operators is introduced to increase the search ability of CSA. The performance of the proposed framework is assessed using a typical hybrid AC-DC microgrid with distributed energy resources. The simulation results show the high efficacy of the proposed method in comparison with other methods.

It is widely accepted that the integration of natural sources in distribution networks is becoming more attractive as they are sustainable and nonpolluting. This paper firstly proposes a modified Manta Ray Foraging Optimizer (MMRFO) to enhance the characteristic of MRFO technique. The modified MRFO technique is based on inserting the Simulated Annealing technique into the original MRFO to enhance the exploitation phase which is responsible for finding the promising region in the search area. Secondly, the developed technique is utilized for determining the best sizes and locations of multiple wind turbine (WT) and photovoltaic (PV) units in Radial Distribution System (RDS). The total system loss is taken as single-objective function to be minimized, considering the probabilistic nature of PV and WT output generation with variable load demand. Reactive loss sensitivity factor (QLSF) is utilized for obtaining the candidate locations up to fifty percent of total system buses with the aim of reducing the search space. Battery Energy Storage System (BESS) is used with PV to change it into a dispatchable supply. The changes in system performance by optimally integrating PV and WT alone or together are comprehensively studied. The proposed solution approach is applied for solving the standard IEEE 69 bus RDS. The obtained results demonstrate that installing PV and WT simultaneously achieves superior results than installing PV alone and WT alone in RDS. Further, simultaneous integration of WT and PV with BESS gives better results than simultaneous integration of WT and PV without BESS in RDS. The simulation results prove that the total system losses can be reduced by enabling the reactive power capability of PV inverters. The convergence characteristic shows that the modified MRFO gives the best solutions compared with the original MRFO algorithm.