Swedish government’s target is to have 100 per cent renewable electricity production by 2040. Currently, hydropower contributes the majority of renewable electricity generation of the country. The wind power capacity has increased significantly in the past decade. In this paper, practical data is used to study the possibility of reaching the 100% renewable electricity generation goal by replacing existing thermal generations with wind power generations. It is found that the Swedish electricity generation system can reach 100% renewable by tripling the existing wind power capacity combined with the existing hydropower in the country. Based on current growth rate of wind power installation, the goal could be reached within 20 years. Hourly simulation shows that 100 % renewable energy generation system composed by wind power and hydropower satisfy hourly operation requirements.
This work presents a generic storage model (GSM) inspired by the scheduling of hydraulic reservoirs. The model for steady state short-term (ST) operational studies interlaces with the long-term (LT) energy scheduling through a piecewise-linear Future Cost Function (FCF). Under the assumption that a Stochastic Dual Dynamic Programming (SDDP) approach has been used to solve the energy schedule for the LT, the FCF output from that study will be processed to obtain an equivalent marginal opportunity cost for the storage unit. The linear characteristic of a segment of the future cost function (FCF) will allow a linear modeling of the storage unit production cost. This formulation will help to coordinate the renewable resource along with storage facilities in order to find the optimal operation cost while meeting end-point conditions for the long-term plan of the energy storage. The generic model will be implemented to represent a battery storage and a pumped-hydro storage. A stochastic unit commitment (SUC) with the GSM will be formulated and tested to assess the day-ahead scheduling strategy of a Virtual Power Plant (VPP) facing uncertainties from production, consumption, and market prices.
Capacity remuneration mechanisms have been originally oriented to ensure availability and continuity of supply on the power generation pool. Equivalent generation-based capacity mechanisms could be implemented to enhance and prolong the usability of the distribution grid. In particular, such capacity mechanisms would provide an alternative to traditional expansion options leading to investment deferral. In this work, a distribution capacity mechanism to fit within a distribution network planning methodology will be proposed and discussed. The capacity mechanism will be outlined following similar guidelines as for the design of capacity mechanisms used in the energy only market. The result of the design is a volume based capacity auction for a capacity-constrained system, oriented to both the active and the reactive power provision.
The long-term (LT) scheduling of reservoir-type hydropower plants is a multistage stochastic dynamic problem that has been traditionally solved using the stochastic dual dynamic programming (SDDP) approach. This LT schedule of releases should be met through short-term (ST) scheduling decisions obtained from a hydro-thermal scheduling that considers uncertainties. Both time scales can be linked if the ST problem considers as input the future cost function (FCF) obtained from LT studies. Known the piecewise-linear FCF, the hydro-scheduling can be solved as a one-stage problem. Under certain considerations a single segment of the FCF can be used to solve the schedule. From this formulation an equivalent model for the hydropower plant can be derived and used in ST studies. This model behaves accordingly to LT conditions to be met, and provides a marginal cost for dispatching the plant. A generation company (GENCO) owning a mix of hydro, wind, and thermal power will be the subject of study where the model will be implemented. The GENCO faces the problem of scheduling the hydraulic resource under uncertainties from e.g. wind and load while determining the market bids that maximize its profit under uncertainties from market prices. A two-stage stochastic unit commitment (SUC) for the ST scheduling implementing the equivalent hydro model will be solved.
As the penetration of uncontrollable renewable energy sources (RESs) increases, energy storage and flexible demand will play a more important role in future power systems. In this paper, air-conditioning systems with thermal energy storage (A/C storage systems) are studied as a way of compensating uncertainties from wind power. Wind power forecast errors are analyzed from different perspectives in order to better assist the schedule of storage devices. An operation scheme is proposed for A/C storage systems for both day-ahead scheduling and real-time operation, based on the features of wind power forecast errors. The targets include load management and compensation of wind power forecast errors. Simulations illustrate the effectiveness of the proposed scheme to support power systems with high wind penetration.
This work presents a linear solution for the short-term hydro-thermal scheduling problem linked to long-term conditions through a piecewise-linear Future Cost Function (FCF). Given end-point conditions to conform long-term water releases, and given actual reservoir conditions, a segment of a pre-built piecewise future cost function will be chosen. The linear characteristic of the FCF segment will allow a linear modeling of the hydro-power plant, in a similar fashion as a thermal unit with an equivalent marginal opportunity cost. A short-term hydro thermal coordination problem will be formulated considering parallel and cascaded hydro-reservoirs. Three study cases involving different reservoir configurations and scenarios will be computed to test the model. The results of this model mimics coherently the future-cost hydro-thermal coordination problem for the different configurations tested. Given similarities with other forms of energy storage, a new theoretical model for generic storage will be proposed and discussed.
The importance of the performance of frequency regulation has already been acknowledged by regulators and Independent System Operators (ISOs). A performance-based frequency regulation market model considering both regulation capacity and regulation mileage constraints is proposed in this paper. In the proposed market, high-performance regulation resources have higher priorities to be selected in the market. Market clearing prices are derived with Lagrange relaxation. The analysis of the components of market clearing prices accurately indicates the correlation between regulation capacity and regulation mileage. To accommodate the proposed regulation market design, AGC allocation algorithm is adjusted based on the market clearing results. The clearing procedure of the market model is demonstrated on an illustrative case. The proposed market design is tested and verified with market simulations and system dynamic simulations. Simulation results are discussed and compared to show the effectiveness of the proposed market design.
This study presents a market model that procures energy and performance-based regulation services simultaneously considering the participation of energy storage devices. The correlations of energy, regulation capacity, and regulation mileage are explicitly demonstrated. The proposed market model determines the energy schedule of generation units, charging and discharging profiles of energy storage devices, and the schedule of regulation services. Market clearing prices for energy, regulation capacity, and regulation mileage are derived and decomposed through Lagrange multiplier analysis. The relationships between the clearing prices of different market products are analysed. The proposed market model is tested and verified with the IEEE 30-bus system. © The Institution of Engineering and Technology.
Under the present European directive concerning common rules for the internal market in electricity, distribution companies are not allowed to own distributed generation (DG) but encouraged to include it as a planning option to defer investment in traditional grid reinforcements. Distribution system operators (DSOs) have used the provision of capacity contracted to DG as a viable alternative under current regulatory arrangements. Here, the topics bonding DSOs and DG owners under the present regulation will be explored and a planning structure that considers distribution capacity contracts as a planning option will be proposed. This will serve as a road map for DSOs to implement its preferred planning tools in an optimisation context, considering costs of investment, reliability, operation, and capacity provision while complying with current regulation.
A Distribution System Operator (DSO) might consider a capacity contract as a planning alternative to defer grid investments. A Virtual Power Plant (VPP) might be able to provide such capacity and change its production as requested by the DSO. This article presents an assessment of the impact of this type of distribution capacity contract (DCC) on VPP's remuneration. This assessment is done by comparing the optimal production / bidding strategy which maximize its profit, under presence or absence of these contracts. The impact of intermittent generation and storage while evaluating these scenarios will be investigated as well. A stochastic unit commitment will be used to determine the VPP's strategy under uncertainties from wind power, load, market prices, and the requested power by the DSO. The model showed that the VPP involvement in distribution capacity contracts can improve its remuneration when certain types of Distributed Energy Resources (DER) are used to provide the service.
Effective charging and vehicle-to-grid (V2G) control strategies can utilise the properties of electric vehicles (EVs) to obtain various benefits. EVs are modelled as individuals in existing charging control algorithms. In this study, a new modelling method of EVs and an optimal charging control strategy are proposed so that all EVs in a control area can be regarded as a single object in the optimisation process. The strategy minimises the total charging cost of EVs, and can be further expanded to serve V2G control. With the new modelling method, the computational burden of the optimisation algorithm can be reduced significantly and does not increase with the number of EVs. Thus the strategy is extremely effective when the number of EVs becomes large, and the implementation cost could be more reasonable since less computational capacity is required. Case studies are presented to illustrate the performance of the strategy.
The distribution system planner should be able to coordinate smart grid solutions in order to find cost effective expansions plans. These plans should be able to deal with new added system uncertainties from renewable production and consumers while guaranteeing power quality and availability of supply. This paper proposes a structure for distribution systems planning oriented to help the planner in deciding how to make use of smart solutions for achieving the described task. Here, the concept of a system planning toolbox is introduced and supported with a review of relevant works implementing smart solutions. These are colligated in a way that the system planner can foresee what to expect with their combined implementation. Future developments in this subject should attempt to theorize a practical algorithm in an optimization and decision making context.
This paper gives a status report of joint working group C4.24. Next to an overview of the different activities started, more details are given of the work done on voltage dips, new sources of emission, feeder reconfiguration, demand side management and power quality and economics.